[Hiring] AI Practice Lead @Inficare Technologies

Role Description

Own end-to-end delivery of AI-native programs from architecture through production.

• Design multi-agent orchestration using LangGraph, CrewAI, or equivalent.
• Integrate agent systems with enterprise systems of record (ERPs, CRMs, data platforms).
• Build and evaluate RAG pipelines - chunking, retrieval, re-ranking, and evaluation.
• Bring AI governance into design from day one: agent identity, privilege scoping, audit, human-in-the-loop, kill switches.
• Advise clients on standing up AI capability - practice structure, operating model, build vs. buy decisions.
• Shape and defend technical proposals, PoC plans, and roadmaps for CXO-level stakeholders.
• Mentor engineers and build AI delivery capability across the organisation.

Qualifications

• Engineering Foundation:
• Data modelling (SQL & NoSQL)
• Event-driven architecture (Kafka, RabbitMQ)
• Distributed systems failure modes
• Caching strategy
• Observability
• API/integration patterns
• CI/CD
• Containers
• AI Engineering:
• Evaluation frameworks
• Error analysis
• Agent debugging
• Context engineering
• Memory strategy
• Tool surface design
• Cost and latency optimisation
• Agent & LLM Tooling:
• LangGraph
• CrewAI
• RAG
• Claude Code
• Cursor
• Codex
• MCP
• Cloud:
• Deep expertise in at least one major cloud platform (AWS, Azure, or GCP)
• Consulting:
• Client leadership
• CXO communication
• Workshop facilitation
• Technical proposal ownership

Requirements

• 12-15 years of experience.

Benefits

• Remote work with travel required for client engagements.