Lead AI Engineer

The candidate will have responsibilities across the following functions:

ML Foundation, Lifecycle and MLOps:

• Own end-to-end ML pipeline design covering data preparation, feature stores, training, evaluation, deployment, and monitoring.
• Instrument and manage model drift, data drift, concept drift, and prediction drift with automated alerting and retraining workflows.
• Define and enforce ML engineering standards across the team: reproducibility, experiment tracking, code quality.

Agentic AI Engineering:

• Architect and build multi-agent systems for complex, stateful, multi-step business workflows.
• Design agent communication patterns, tool registries, memory management, and failure recovery.
• Move beyond framework defaults; understand what the orchestration layer is doing and when to build custom.
• Evaluate and select agentic frameworks based on problem requirements, not familiarity.

GenAI and Foundation Model Engineering:

• Select, fine-tune, and serve foundation models appropriate to the problem; default to fit-for-purpose, not to the most capable or most expensive option.
• Manage prompt-to-model drift when a model version changes, understand what breaks and own the evaluation process.
• Design structured prompt engineering workflows with evaluation harnesses, not ad hoc iteration.

RAG and Knowledge Systems:

• Netscribes Analytics | Data and AI Centre of Excellence Page 2JOB.
• DESCRIPTION | Lead Consultant, AI Engineering | Data and AI CoE.
• Design retrieval systems from requirements: choose between naive RAG, hybrid, Graph RAG, or knowledge graph-based approaches based on the problem, not the template.
• Select and configure vector databases appropriate to the retrieval use case; understand latency, scale, and cost trade-offs.
• Own retrieval quality end-to-end: relevance scoring, hallucination benchmarking, golden set evaluation.
• Progressively build competency and lead implementation in Graph.
• RAG and structured knowledge retrieval.

LLM Evaluation and Quality:

• Define and own the evaluation framework for all LLM and agentic AI solutions from unit evals through end-to-end system testing.
• Implement LLM Eval tooling (RAGAS, TruLens, DeepEval or equivalent) as a standard part of the delivery pipeline, not a post-hoc check.
• Establish golden datasets and ground truth benchmarks per domain; maintain and evolve them as models and prompts change.
• Report evaluation outcomes clearly to both technical teams and client stakeholders.

Technical Leadership and Delivery:

• Set the engineering bar for the AI/GenAI team code reviews, architecture decisions, and design standards.
• Work directly with pre-sales and solution design where technical depth is required in client engagements.
• Mentor mid-level engineers; translate ambiguous business requirements into clear engineering specifications.
• Represent the team credibly in technical client conversations across governance, architecture, and delivery.
• Education: Graduates only (please ignore pursuing/drop out/12th or 10th pass).