Job Description: Senior AI Engineer — Multi-Agent Platform
Company Name: Leading AI Based Service company
Job Title / Role: Senior AI Engineer
Type of Employment: Full Time & C2C
Experience Required: 3–9 years
Number of Positions: 6
CTC: 30–60 LPA
Work Location: Bangalore (Preference given to Bangalore - Kothanur)
Mode of Work: Remote
Notice Period / Start Date: 0–30 days (Serving Notice Period only)
About the Project & Role
We are building a multi-agent orchestration platform for enterprise document and transaction
processing. The technical core is durable orchestration—long-running workflows that pause
for human approval for hours or days, recover from tool failures, and replan without losing
state. Agents read unstructured documents, classify against complex taxonomies, and write
into legacy systems through typed connectors spanning mainframe terminal emulation, EDI,
and JDBC.
Key Responsibilities & What You'll Do
• Build and own sub-agents: typed input/output schemas, versioned prompts, a
constrained tool set, and a published success metric for each.
• Design the orchestration layer — planning, replanning, tool-failure recovery, and
handoff between supervisor and sub-agents.
• Build the eval harness that decides when an agent is safe to promote from shadow to
assisted to autonomous.
• Work the two-tier model setup: a heavy model for judgment and classification, a
lighter self-hosted tier for high-volume extraction and formatting.
• Make agent behaviour observable and debuggable — traces, cost per agent, failure
attribution.
Must-Have Qualifications & Requirements
• Production Experience: You've shipped LLM systems into production, not just
demos. You can talk about what broke and what you changed.
• Orchestration: Comfortable with agent orchestration in some form — LangGraph,
custom runtimes, workflow engines, or your own. We care more about the reasoning
than the library.
Programming Languages: Strong Python. Go or TypeScript useful.
Evaluation: You've built evals, or you've felt the pain of not having them.
Reliability: Solid on structured output, schema validation, and getting reliable
behaviour out of unreliable models.
Good-to-Have Skills
Durable execution experience — Temporal, Cadence, Step Functions, or similar.
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