Opportunity 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 modern 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 useful.

- 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