Chance Description AI Engineer

Our end client a technology-driven organization focused on data engineering, AI, cloud solutions, and digital transformation — delivers innovative technology solutions that help businesses modernize their data infrastructure, leverage artificial intelligence, and build scalable digital platforms. The organization works on advanced technology initiatives across enterprise and data-intensive environments.

They are looking for an experienced AI Engineer to design, develop, and deploy intelligent AI/ML solutions, working with up-to-date AI technologies to solve complex business challenges. This is a high-impact opportunity for professionals who enjoy building production-ready AI systems, working with cross-functional teams, and contributing to next-generation AI-driven solutions.

Role & responsibilities
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 behavior observable and debuggable traces, cost per agent, failure

attribution

Preferred candidate profile
You've shipped LLM systems into production, not just demos. You can talk about

what broke and what you changed.
Comfortable with agent orchestration in some form — LangGraph, custom runtimes, workflow engines, or your own. We care more about the reasoning than the library.
Robust Python. Go or TypeScript practical.
You've built evals, or you've felt the pain of not having them.
Solid on structured output, schema validation, and getting reliable behavior out of unreliable models.

Nice to have
Durable execution experience — Temporal, Cadence, Step Functions, or similar
Self-hosted inference: vLLM, TGI, quantisation, GPU scheduling
Retrieval systems at scale, and knowing when not to use them
Document understanding: OCR, layout parsing, extraction from messy PDFs
Anything regulated — audit trails, approval workflows, compliance reporting

📌 Artificial Intelligence Engineer Bangalore Metropolitan Area
🏢 hiringhood
📍 Bangalore Metropolitan Area