[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.

Preferred Skills

• Experience building reusable AI frameworks and practice accelerators.
• Background evaluating emerging models and frameworks before mainstream adoption.
• Prior experience in a technology services or consulting organisation.

Location & Work Model

East Coast, USA - Remote with travel required for client engagements.