AI/ML Engineer — Agentic Systems (ID 1423)

• 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 new 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.