Senior AI Lead

Senior AI Lead

Incedo Platform & Solutions| brAInspark & Enterprise AI Products

LOCATION: Gurugram

EXPERIENCE: 8–12 years

REPORTS TO: Engineering Manager

ABOUT INCEDO

Incedo is a global AI and data transformation specialist, helping companies turn digital investment into sustainable business impact by delivering ROI from AI@Scale. We are 4,000+ people across the US, Canada, Latin America and India, working with Fortune 500 enterprises and fast-growing clients in banking & payments, wealth management, telecom, hi-tech and life sciences.

Build what's next in AI, data and enterprise platforms

Our Platform & Solutions portfolio is where Incedo builds AI-native products for real enterprise problems: Incedo Lighthouse (AI-powered decision intelligence), DataXel (agentic data modernization), brAInspark (agentic AI enablement), IncedoPay (integrated payables), Kratos (regulatory compliance and data control for banking) and DQXpert (AI-powered data quality) — plus domain products across customer support, document processing, quality engineering, healthcare and financial services.

WHY THIS ROLE

brAInspark is a live agentic AI platform that enterprises deploy multi-agent systems on. What you build runs inside their businesses, against their data, under their compliance regimes. You decide what the platform may do autonomously — and then prove it is safe to do it.

THE ROLE

You lead the AI engineering team behind Incedo's agentic and GenAI products. You own the AI technology roadmap, the reference architecture for agentic systems across the portfolio, and the engineering standards the team builds to.

This is a hands-on role. You will be in the code, in the evals and in the architecture reviews — not only in the roadmap deck.

WHAT YOU'LL OWN

1. Agentic and GenAI system design

• Build production RAG, agentic and LLM-based systems — multi-agent orchestration, tool use, memory and MCP-based enterprise integration.
• Define the agent reference architecture across Incedo's platforms, so teams build on shared patterns rather than one per product.
• Evaluate and integrate foundation models — what to call, what to fine-tune, and what should never be a model at all.

2. Evaluation, quality and governance

• Own the evaluation strategy: offline evals, regression suites, human review loops and production LLM observability.
• Establish guardrails — hallucination mitigation, PII-safe prompting, cost and latency budgets, and graceful fallbacks when confidence drops.
• Set AI engineering standards for prompt versioning, reproducibility, testing and code quality.

3. Platform and delivery

• Take solutions from prototype to production on cloud-native infrastructure.
• Partner with Product, Solution Architecture and Engineering to turn roadmap intent into shippable increments.

4. Team leadership

• Lead, mentor and grow the AI engineering team; run technical reviews and raise the technical bar.
• Contribute to hiring and capability building as the team scales.

WHAT YOU'LL BRING

• 8+ years in software engineering, including 4+ years building AI/ML systems — and still hands-on.
• Strong programming depth (Python preferred) with production-grade, reviewable code.
• LLM and GenAI systems shipped to production, not only prototyped.
• Agent frameworks in practice: LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen or LlamaIndex.
• RAG done properly: retrieval strategy, embeddings, vector databases — and diagnosing a weak pipeline.
• Model platform fluency: Azure AI / OpenAI APIs, AWS Bedrock or Google Vertex AI.
• Cloud-native delivery: Docker, Kubernetes, CI/CD.
• Engineering judgement — knowing when a deterministic solution beats a model, and saying so.

GOOD TO HAVE

• AI features built inside a product company, not only in client delivery.
• MCP, multi-agent systems and agent interoperability patterns.
• Model evaluation tooling, observability and MLOps/LLMOps at scale; Azure AI Engineer, AWS ML Specialty or equivalent certification.

EDUCATION

B.E. / B.Tech / M.Tech / M.S. in Computer Science, Engineering or a related technical discipline. Equivalent industry experience considered.