← Back to history

Pipeline run

2dc2c0f2-4f85-412b-8844-69a89df3af86

Pipeline LLM cost (USD)
API 1: $0.0044 API 2: $0.0000 API 3: $0.0000 Total: $0.0044

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · Applied AI Engineer
Build and productionize AI systems for document intelligence, retrieval, and agent workflows, with emphasis on OCR/layout parsing, LLM integration, and reliable deployment/monitoring for financial workflows.
"work across the core systems that power our product: document intelligence, retrieval, agentic workflows, and the infrastructure required to deploy them reliably in production"
Tech stack maturity
AI-Native & Bleeding-Edge
The skill set centers on cutting-edge AI engineering for LLMs/VLMs, structured outputs, document processing, evaluation, and observability across cloud platforms, which aligns with an AI-native modern stack.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
3.20 / 5
✓ Title match
✓ Has AI skill
✓ AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1): Claude, Cursor
Frameworks (×2): —
Models / concepts (×3): RAG, LLMs, agentic, multimodal, AI
Evidence — skills matched in JD (27)
Python TypeScript LLMs Prompting Context Management Structured Outputs Document Processing VLMs OCR Layout Parsing Containers CI/CD Azure GCP Evaluation Observability Failure Analysis RAG Hybrid Retrieval Reranking Vision-Language Models Multimodal Document Understanding Agentic Systems Agent Tooling Claude Code +2
Skill cluster (6 dimension groups, role-scoped)
RAG Architectures
Context Management Hybrid Retrieval Reranking
Cloud Platforms for AI Deployment
Azure GCP
JavaScript and TypeScript
TypeScript
Python Programming
Python
Structured Output Integration
Structured Outputs
Cross-cutting / unaligned
LLMs Prompting Document Processing VLMs OCR Layout Parsing Containers CI/CD Evaluation Observability Failure Analysis RAG Vision-Language Models Multimodal Document Understanding Agentic Systems Agent Tooling Claude Code Cursor Codex
Show KRA description ↓
As an Applied AI Engineer at Bynd, you will work across the core systems that power our product: document intelligence, retrieval, agentic workflows, and the infrastructure required to deploy them reliably in production. You will build systems that financial institutions depend on for high-accuracy extraction, analysis, and workflow automation. Must-haves Strong programming ability in Python and TypeScript Experience integrating LLMs into production systems, including prompting, context management, structured outputs, and cost-performance tradeoffs Experience building or working with document processing systems such as VLMs for OCR and layout parsing models Comfort with cloud deployment and production systems, including containers, CI/CD, and Azure or GCP Experience thinking carefully about system quality, including evaluation, observability, or failure analysis for complex AI workflows Preferred Experience with RAG systems, hybrid retrieval, reranking, and eval design Experience with vision-language models or multimodal document understanding Familiarity with Azure- or GCP-based AI infrastructure Familiarity with financial services workflows such as investment banking, private equity, equity research, credit, or diligence Experience building multi-step agentic systems or using modern agent tooling You thrive in fast-moving environments and care deeply about the quality of what you build. You are ambitious and energized by difficult problems. You like working on things that are technically hard, operationally messy, and valuable when solved well. You are AI-native in how you work. Tools like Claude Code, Cursor, Codex, and modern model APIs are part of your everyday workflow. You know these tools are powerful, but you also understand where they fail and how to build with judgment around them. You are an owner. You are autonomous, self-directed, and comfortable with ambiguity. You take responsibility for outcomes, not just tasks. You are curious about the domain. You want to understand how financial professionals actually work, what makes a workflow painful, what accuracy really means in context, and why a product decision matters.

Signals

Skill full-stack-engineer
0.15
Alias ml-engineer
1.00
KRA ai-compliance-officer
0.45

Post-classification

Centroidupdated · n=17
Alias collision log—
New-role queue—
New skills captured18
New KRA captured—

Captured for admin review

Prompting primary ↔ AI Engineer pending
Context Management primary ↔ AI Engineer pending
Document Processing primary ↔ AI Engineer pending
VLMs primary ↔ AI Engineer pending
OCR primary ↔ AI Engineer pending
Layout Parsing primary ↔ AI Engineer pending
Containers primary ↔ AI Engineer pending
Evaluation primary ↔ AI Engineer pending
Observability primary ↔ AI Engineer pending
Failure Analysis primary ↔ AI Engineer pending
Hybrid Retrieval ↔ AI Engineer pending
Vision-Language Models ↔ AI Engineer pending
Multimodal Document Understanding ↔ AI Engineer pending
Agentic Systems ↔ AI Engineer pending
Agent Tooling ↔ AI Engineer pending
Claude Code ↔ AI Engineer pending
Cursor ↔ AI Engineer pending
Codex ↔ AI Engineer pending
Status: extract_from_jd_done Created: 2026-05-19T18:24:24.866009Z Updated: 2026-05-19T18:24:26.325049Z
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

No chosen role stored for this run.

Job description

Why Bynd



Bynd is building the intelligence layer for financial services.


We work with leading investment banks, private equity firms, asset managers, lenders, and advisory teams to transform how they extract, analyze, and act on information buried across financial documents, filings, reports, and internal workflows.


We operate with the standards of a research team and the urgency of a product company. We care deeply about technical quality, product taste, and building things that get used.


The Role



As an Applied AI Engineer at Bynd, you will work across the core systems that power our product: document intelligence, retrieval, agentic workflows, and the infrastructure required to deploy them reliably in production.


You will build systems that financial institutions depend on for high-accuracy extraction, analysis, and workflow automation.


What You Will Need



Must-haves

Strong programming ability in Python and TypeScript
Experience integrating LLMs into production systems, including prompting, context management, structured outputs, and cost-performance tradeoffs
Experience building or working with document processing systems such as VLMs for OCR and layout parsing models
Comfort with cloud deployment and production systems, including containers, CI/CD, and Azure or GCP
Experience thinking carefully about system quality, including evaluation, observability, or failure analysis for complex AI workflows


Preferred

Experience with RAG systems, hybrid retrieval, reranking, and eval design
Experience with vision-language models or multimodal document understanding
Familiarity with Azure- or GCP-based AI infrastructure
Familiarity with financial services workflows such as investment banking, private equity, equity research, credit, or diligence
Experience building multi-step agentic systems or using modern agent tooling


Who You Are

You thrive in fast-moving environments and care deeply about the quality of what you build.
You are ambitious and energized by difficult problems. You like working on things that are technically hard, operationally messy, and valuable when solved well.
You are AI-native in how you work. Tools like Claude Code, Cursor, Codex, and modern model APIs are part of your everyday workflow. You know these tools are powerful, but you also understand where they fail and how to build with judgment around them.
You are an owner. You are autonomous, self-directed, and comfortable with ambiguity. You take responsibility for outcomes, not just tasks.
You are curious about the domain. You want to understand how financial professionals actually work, what makes a workflow painful, what accuracy really means in context, and why a product decision matters.

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

Python Primary No API 2 row (run stopped after API 1 or history missing)
TypeScript Primary No API 2 row (run stopped after API 1 or history missing)
LLMs Primary No API 2 row (run stopped after API 1 or history missing)
Prompting Primary No API 2 row (run stopped after API 1 or history missing)
Context Management Primary No API 2 row (run stopped after API 1 or history missing)
Structured Outputs Primary No API 2 row (run stopped after API 1 or history missing)
Document Processing Primary No API 2 row (run stopped after API 1 or history missing)
VLMs Primary No API 2 row (run stopped after API 1 or history missing)
OCR Primary No API 2 row (run stopped after API 1 or history missing)
Layout Parsing Primary No API 2 row (run stopped after API 1 or history missing)
Containers Primary No API 2 row (run stopped after API 1 or history missing)
CI/CD Primary No API 2 row (run stopped after API 1 or history missing)
Azure Primary No API 2 row (run stopped after API 1 or history missing)
GCP Primary No API 2 row (run stopped after API 1 or history missing)
Evaluation Primary No API 2 row (run stopped after API 1 or history missing)
Observability Primary No API 2 row (run stopped after API 1 or history missing)
Failure Analysis Primary No API 2 row (run stopped after API 1 or history missing)
RAG Secondary No API 2 row (run stopped after API 1 or history missing)
Hybrid Retrieval Secondary No API 2 row (run stopped after API 1 or history missing)
Reranking Secondary No API 2 row (run stopped after API 1 or history missing)
Vision-Language Models Secondary No API 2 row (run stopped after API 1 or history missing)
Multimodal Document Understanding Secondary No API 2 row (run stopped after API 1 or history missing)
Agentic Systems Secondary No API 2 row (run stopped after API 1 or history missing)
Agent Tooling Secondary No API 2 row (run stopped after API 1 or history missing)
Claude Code Secondary No API 2 row (run stopped after API 1 or history missing)
Cursor Secondary No API 2 row (run stopped after API 1 or history missing)
Codex Secondary No API 2 row (run stopped after API 1 or history missing)

Library artifacts (this run)

No artifact rows for this run.
nano JD Parser — gpt-4.1-nano click to toggle
RoleApplied AI Engineer
CompanyBynd
DomainFinancial Services
JD type pass
Show raw JSON 
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "Bynd is building the intelligence",
      "last_5_words": "technical quality, product taste, and building"
    },
    "text": "Bynd is building the intelligence layer for financial services.\n\nWe work with leading investment banks, private equity firms, asset managers, lenders, and advisory teams to transform how they extract, analyze, and act on information buried across financial documents, filings, reports, and internal workflows.\n\nWe operate with the standards of a research team and the urgency of a product company. We care deeply about technical quality, product taste, and building things that get used.",
    "word_count": 64
  },
  "certifications": [],
  "company_name": "Bynd",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "Finance",
        "FinTech"
      ],
      "domain": "Financial Services"
    },
    "secondary": null
  },
  "education": [],
  "experience": null,
  "job_locations": [],
  "role": "Applied AI Engineer",
  "role_aliases": [
    "AI Engineer",
    "Machine Learning Engineer",
    "Applied AI Developer"
  ],
  "role_archetype": "Engineering",
  "roles_and_responsibilities": [
    {
      "bullet_count": 0,
      "heading": "The Role",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "As an Applied AI Engineer",
        "last_5_words": "extraction, analysis, and workflow automation."
      },
      "text": "As an Applied AI Engineer at Bynd, you will work across the core systems that power our product: document intelligence, retrieval, agentic workflows, and the infrastructure required to deploy them reliably in production.\n\nYou will build systems that financial institutions depend on for high-accuracy extraction, analysis, and workflow automation.",
      "word_count": 47
    },
    {
      "bullet_count": 14,
      "heading": "What You Will Need",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Must-haves\n\nStrong programming ability",
        "last_5_words": "or using modern agent tooling"
      },
      "text": "Must-haves\n\nStrong programming ability in Python and TypeScript\nExperience integrating LLMs into production systems, including prompting, context management, structured outputs, and cost-performance tradeoffs\nExperience building or working with document processing systems such as VLMs for OCR and layout parsing models\nComfort with cloud deployment and production systems, including containers, CI/CD, and Azure or GCP\nExperience thinking carefully about system quality, including evaluation, observability, or failure analysis for complex AI workflows\n\nPreferred\n\nExperience with RAG systems, hybrid retrieval, reranking, and eval design\nExperience with vision-language models or multimodal document understanding\nFamiliarity with Azure- or GCP-based AI infrastructure\nFamiliarity with financial services workflows such as investment banking, private equity, equity research, credit, or diligence\nExperience building multi-step agentic systems or using modern agent tooling",
      "word_count": 174
    },
    {
      "bullet_count": 0,
      "heading": "Who You Are",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "You thrive in fast-moving environments",
        "last_5_words": "accuracy really means in context, and why"
      },
      "text": "You thrive in fast-moving environments and care deeply about the quality of what you build.\nYou are ambitious and energized by difficult problems. You like working on things that are technically hard, operationally messy, and valuable when solved well.\nYou are AI-native in how you work. Tools like Claude Code, Cursor, Codex, and modern model APIs are part of your everyday workflow. You know these tools are powerful, but you also understand where they fail and how to build with judgment around them.\nYou are an owner. You are autonomous, self-directed, and comfortable with ambiguity. You take responsibility for outcomes, not just tasks.\nYou are curious about the domain. You want to understand how financial professionals actually work, what makes a workflow painful, what accuracy really means in context, and why a product decision matters.",
      "word_count": 116
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle 
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": true,
      "skill_name": "TypeScript"
    },
    {
      "is_primary": true,
      "skill_name": "LLMs"
    },
    {
      "is_primary": true,
      "skill_name": "Prompting"
    },
    {
      "is_primary": true,
      "skill_name": "Context Management"
    },
    {
      "is_primary": true,
      "skill_name": "Structured Outputs"
    },
    {
      "is_primary": true,
      "skill_name": "Document Processing"
    },
    {
      "is_primary": true,
      "skill_name": "VLMs"
    },
    {
      "is_primary": true,
      "skill_name": "OCR"
    },
    {
      "is_primary": true,
      "skill_name": "Layout Parsing"
    },
    {
      "is_primary": true,
      "skill_name": "Containers"
    },
    {
      "is_primary": true,
      "skill_name": "CI/CD"
    },
    {
      "is_primary": true,
      "skill_name": "Azure"
    },
    {
      "is_primary": true,
      "skill_name": "GCP"
    },
    {
      "is_primary": true,
      "skill_name": "Evaluation"
    },
    {
      "is_primary": true,
      "skill_name": "Observability"
    },
    {
      "is_primary": true,
      "skill_name": "Failure Analysis"
    },
    {
      "is_primary": false,
      "skill_name": "RAG"
    },
    {
      "is_primary": false,
      "skill_name": "Hybrid Retrieval"
    },
    {
      "is_primary": false,
      "skill_name": "Reranking"
    },
    {
      "is_primary": false,
      "skill_name": "Vision-Language Models"
    },
    {
      "is_primary": false,
      "skill_name": "Multimodal Document Understanding"
    },
    {
      "is_primary": false,
      "skill_name": "Agentic Systems"
    },
    {
      "is_primary": false,
      "skill_name": "Agent Tooling"
    },
    {
      "is_primary": false,
      "skill_name": "Claude Code"
    },
    {
      "is_primary": false,
      "skill_name": "Cursor"
    },
    {
      "is_primary": false,
      "skill_name": "Codex"
    }
  ],
  "jd_role": {
    "display_name": "Applied AI Engineer",
    "rationale": null,
    "role_aliases": [
      "AI Engineer",
      "Machine Learning Engineer",
      "Applied AI Developer"
    ],
    "role_archetype": "Engineering",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "Bynd is building the intelligence",
        "last_5_words": "technical quality, product taste, and building"
      },
      "text": "Bynd is building the intelligence layer for financial services.\n\nWe work with leading investment banks, private equity firms, asset managers, lenders, and advisory teams to transform how they extract, analyze, and act on information buried across financial documents, filings, reports, and internal workflows.\n\nWe operate with the standards of a research team and the urgency of a product company. We care deeply about technical quality, product taste, and building things that get used.",
      "word_count": 64
    },
    "certifications": [],
    "company_name": "Bynd",
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "Finance",
          "FinTech"
        ],
        "domain": "Financial Services"
      },
      "secondary": null
    },
    "education": [],
    "experience": null,
    "job_locations": [],
    "role": "Applied AI Engineer",
    "role_aliases": [
      "AI Engineer",
      "Machine Learning Engineer",
      "Applied AI Developer"
    ],
    "role_archetype": "Engineering",
    "roles_and_responsibilities": [
      {
        "bullet_count": 0,
        "heading": "The Role",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "As an Applied AI Engineer",
          "last_5_words": "extraction, analysis, and workflow automation."
        },
        "text": "As an Applied AI Engineer at Bynd, you will work across the core systems that power our product: document intelligence, retrieval, agentic workflows, and the infrastructure required to deploy them reliably in production.\n\nYou will build systems that financial institutions depend on for high-accuracy extraction, analysis, and workflow automation.",
        "word_count": 47
      },
      {
        "bullet_count": 14,
        "heading": "What You Will Need",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Must-haves\n\nStrong programming ability",
          "last_5_words": "or using modern agent tooling"
        },
        "text": "Must-haves\n\nStrong programming ability in Python and TypeScript\nExperience integrating LLMs into production systems, including prompting, context management, structured outputs, and cost-performance tradeoffs\nExperience building or working with document processing systems such as VLMs for OCR and layout parsing models\nComfort with cloud deployment and production systems, including containers, CI/CD, and Azure or GCP\nExperience thinking carefully about system quality, including evaluation, observability, or failure analysis for complex AI workflows\n\nPreferred\n\nExperience with RAG systems, hybrid retrieval, reranking, and eval design\nExperience with vision-language models or multimodal document understanding\nFamiliarity with Azure- or GCP-based AI infrastructure\nFamiliarity with financial services workflows such as investment banking, private equity, equity research, credit, or diligence\nExperience building multi-step agentic systems or using modern agent tooling",
        "word_count": 174
      },
      {
        "bullet_count": 0,
        "heading": "Who You Are",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "You thrive in fast-moving environments",
          "last_5_words": "accuracy really means in context, and why"
        },
        "text": "You thrive in fast-moving environments and care deeply about the quality of what you build.\nYou are ambitious and energized by difficult problems. You like working on things that are technically hard, operationally messy, and valuable when solved well.\nYou are AI-native in how you work. Tools like Claude Code, Cursor, Codex, and modern model APIs are part of your everyday workflow. You know these tools are powerful, but you also understand where they fail and how to build with judgment around them.\nYou are an owner. You are autonomous, self-directed, and comfortable with ambiguity. You take responsibility for outcomes, not just tasks.\nYou are curious about the domain. You want to understand how financial professionals actually work, what makes a workflow painful, what accuracy really means in context, and why a product decision matters.",
        "word_count": 116
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "2dc2c0f2-4f85-412b-8844-69a89df3af86",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "ML Engineer",
        "matched_count": null,
        "role_id": 3,
        "score": 1.0,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "AI Engineer",
        "matched_count": null,
        "role_id": 13,
        "score": 1.0,
        "slug": "ai-engineer",
        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "AI Compliance Officer",
        "matched_count": null,
        "role_id": 12,
        "score": 0.4515,
        "slug": "ai-compliance-officer",
        "total_count": null
      },
      {
        "display_name": "AI Engineer",
        "matched_count": null,
        "role_id": 13,
        "score": 0.4285,
        "slug": "ai-engineer",
        "total_count": null
      },
      {
        "display_name": "Android Engineer",
        "matched_count": null,
        "role_id": 4,
        "score": 0.4098,
        "slug": "android-engineer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "matched_count": null,
        "role_id": 3,
        "score": 0.4002,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "AR/VR Engineer",
        "matched_count": null,
        "role_id": 8,
        "score": 0.3888,
        "slug": "ar-vr-engineer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Full Stack Engineer",
        "matched_count": 4,
        "role_id": 15,
        "score": 0.1481,
        "slug": "full-stack-engineer",
        "total_count": 27
      },
      {
        "display_name": "ML Engineer",
        "matched_count": 4,
        "role_id": 3,
        "score": 0.1481,
        "slug": "ml-engineer",
        "total_count": 27
      },
      {
        "display_name": "Backend Engineer",
        "matched_count": 4,
        "role_id": 1,
        "score": 0.1481,
        "slug": "backend-engineer",
        "total_count": 27
      },
      {
        "display_name": "AI Engineer",
        "matched_count": 4,
        "role_id": 13,
        "score": 0.1481,
        "slug": "ai-engineer",
        "total_count": 27
      },
      {
        "display_name": "Cybersecurity Engineer",
        "matched_count": 3,
        "role_id": 5,
        "score": 0.1111,
        "slug": "cybersecurity-engineer",
        "total_count": 27
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "A",
    "chosen_role": {
      "display_name": "AI Engineer",
      "matched_count": null,
      "role_id": 13,
      "score": 1.0,
      "slug": "ai-engineer",
      "total_count": null
    },
    "confidence": 0.4285,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "queued": false,
    "reasoning": "Stage 1 title \u0027AI Engineer\u0027 (alias match, sim 1.00); KRA agrees (0.43)"
  },
  "stage5_updates": {
    "centroid_n_after": 17,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 1292,
        "role_display_name": "AI Engineer",
        "role_slug": "ai-engineer",
        "skill_name": "Prompting",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1293,
        "role_display_name": "AI Engineer",
        "role_slug": "ai-engineer",
        "skill_name": "Context Management",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1294,
        "role_display_name": "AI Engineer",
        "role_slug": "ai-engineer",
        "skill_name": "Document Processing",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1295,
        "role_display_name": "AI Engineer",
        "role_slug": "ai-engineer",
        "skill_name": "VLMs",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1296,
        "role_display_name": "AI Engineer",
        "role_slug": "ai-engineer",
        "skill_name": "OCR",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1297,
        "role_display_name": "AI Engineer",
        "role_slug": "ai-engineer",
        "skill_name": "Layout Parsing",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1298,
        "role_display_name": "AI Engineer",
        "role_slug": "ai-engineer",
        "skill_name": "Containers",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1299,
        "role_display_name": "AI Engineer",
        "role_slug": "ai-engineer",
        "skill_name": "Evaluation",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1300,
        "role_display_name": "AI Engineer",
        "role_slug": "ai-engineer",
        "skill_name": "Observability",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1301,
        "role_display_name": "AI Engineer",
        "role_slug": "ai-engineer",
        "skill_name": "Failure Analysis",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 1302,
        "role_display_name": "AI Engineer",
        "role_slug": "ai-engineer",
        "skill_name": "Hybrid Retrieval",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 1303,
        "role_display_name": "AI Engineer",
        "role_slug": "ai-engineer",
        "skill_name": "Vision-Language Models",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 1304,
        "role_display_name": "AI Engineer",
        "role_slug": "ai-engineer",
        "skill_name": "Multimodal Document Understanding",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 1305,
        "role_display_name": "AI Engineer",
        "role_slug": "ai-engineer",
        "skill_name": "Agentic Systems",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 1306,
        "role_display_name": "AI Engineer",
        "role_slug": "ai-engineer",
        "skill_name": "Agent Tooling",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 1307,
        "role_display_name": "AI Engineer",
        "role_slug": "ai-engineer",
        "skill_name": "Claude Code",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 1308,
        "role_display_name": "AI Engineer",
        "role_slug": "ai-engineer",
        "skill_name": "Cursor",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 1309,
        "role_display_name": "AI Engineer",
        "role_slug": "ai-engineer",
        "skill_name": "Codex",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}
API 2 — extract-details 
{}
API 3 — final-role-output 
{}

LLM Calls

Every model call made for this run, in pipeline order. Click a card to see the model's response.

Loading…