AI/ML Lead

AI/ML Lead

Role Purpose: Design context-engineered prompts, reasoning skills, and validation logic on AWS Bedrock that convert deterministic scan/graph findings into high-quality, defensible migration and remediation dispositions.

Role Summary:

• Owns prompt and reasoning-skill design for turning raw findings into recommendations, built on Bedrock.
• Builds validation/evaluation logic (hallucination mitigation, confidence scoring) so every LLM-generated disposition can be defended and traced back to evidence.
• Works closely with the Graph/RAG Engineer to consume graph outputs as reasoning context.
• Focuses on reasoning quality and defensibility — not the underlying graph/RAG infrastructure itself.

Key Responsibilities:

• Design context-engineered prompts and reusable "skills" on Bedrock for dispositioning, impact analysis, and remediation recommendations.
• Build evaluation and validation frameworks (e.g., LangSmith, TruLens, RAGAS-style approaches) that check LLM outputs against deterministic findings for accuracy and defensibility.
• Convert deterministic (rule-based/graph) findings into natural-language, evidence-backed dispositions engineers can act on.
• Identify and reduce hallucination, low-confidence, or unsupported recommendations.
• Support ID de-collision reasoning — distinguishing true ID collisions from co-mingled data at the table level.
• Iterate prompt/skill design across scan rounds as new patterns and edge cases emerge.

Required Skills:

• Amazon Bedrock (mandatory — Claude/Titan models, Bedrock Agents, Bedrock Knowledge Bases)
• Prompt engineering and context engineering
• LLM reasoning and agentic reasoning design (LangGraph, CrewAI, or equivalent)
• Output validation and evaluation frameworks (LangSmith, TruLens, RAGAS or similar)
• Working with deterministic + probabilistic hybrid systems

Relevant Experience:

• 6+ years in AI/ML engineering with 2+ years focused on LLM prompt/context engineering on AWS Bedrock specifically.
• Experience converting rule-based or graph-based findings into LLM-assisted recommendations.
• Familiarity with hallucination mitigation, confidence scoring, and human-in-the-loop review design.
• Financial services domain exposure is a plus but not mandatory for this role.

Expected Deliverables / Work Products:

• Context-engineered prompt/skill library (Bedrock-based)
• LLM validation and confidence-scoring framework
• Disposition quality audit reports
• Reasoning workflow documentation

Mandatory Skills: Large Language Model LLMOps. Experience: 8-10 Years.

The expected compensation for this role ranges from $80,000 to $158,000. Final compensation will depend on various factors, including your geographical location, minimum wage obligations, skills, and relevant experience. Based on the position, the role is also eligible for Wipro's standard benefits including a full range of medical and dental benefits options, disability insurance, paid time off (inclusive of sick leave), other paid and unpaid leave options.

Wipro provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. Applications from veterans and people with disabilities are explicitly welcome.