- Design and develop agentic AI systems that can verify their own outputs and recover from failures.

- Develop and optimize LLM-based agent workflows, including prompt design, tool use, structured outputs, retrieval, and context management.

- Write and review SQL queries against undocumented production database schemas and determine whether the available data supports specific conclusions.

- Understand client business contexts and identify when AI-generated results may be incorrect or unreliable.

- Build evaluation frameworks by defining success criteria, implementing measurements, and monitoring system accuracy.

- Investigate cases where systems complete successfully but generate valid-looking yet incorrect outputs.

- Evaluate the effectiveness of deterministic checks, safeguards, retries, and validation mechanisms.

- Work directly with business users to convert vague issues such as “this looks wrong” into specific, reproducible technical problems.

- Diagnose root causes using actual error data and implement appropriate fixes.

- Deploy and maintain reliable production agentic AI systems and improve their post-launch reliability.

- Work independently on technically ambiguous problems and continuously learn recent AI tools and techniques.

Requirements

- Hands-on experience in LLM engineering and Agentic AI, beyond basic chatbot development.




- Strong understanding of structured outputs, tool calling, retrieval, context management, and prompt engineering.
- Ability to design prompts as versioned engineering artefacts rather than ad-hoc prompts.
- Strong SQL and database skills, particularly the ability to work with inconsistent or undocumented production schemas.
- Strong analytical and debugging skills, with the ability to identify the actual root cause from error data.
- Ability to evaluate AI system outputs and verify claims before accepting or presenting them.
- Experience building AI/LLM evaluation frameworks, success criteria, measurement systems, and reliability metrics.
- Ability to understand business requirements and translate user-reported problems into reproducible technical issues.
- Strong technical judgment regarding safeguards, validation checks, retries, and cost-versus-accuracy trade-offs.
- Experience with multi-tenant system architecture and security would be an added advantage.
- Strong independent problem-solving ability and willingness to learn rapidly in a fast-changing AI environment.
- A formal degree or fixed number of years of experience is not mandatory; the JD emphasizes demonstrated technical judgment and practical evidence of capability.

📌 AI/ML Engineer — Agentic Systems (India)
🏢 MarketScope
📍 India