Lead AI Engineer

We are looking for a highly experienced Lead AI Architect to lead the design and implementation of next-generation AI platforms powered by Large Language Models (LLMs), Agentic AI, and enterprise-grade AI architectures. The ideal candidate will have extensive experience designing scalable AI systems, defining AI strategy, leading architecture decisions, and mentoring engineering teams. You will work closely with business leaders, product teams, data scientists, and engineering teams to build intelligent AI products capable of autonomous reasoning, planning, orchestration, and decision-making.
The candidate will have responsibilities across the following functions:
AI Architecture and Strategy:
Define enterprise AI architecture and long-term AI technology roadmap.
Design scalable, secure, and production-ready AI platforms.
Architect Agentic AI solutions capable of planning, reasoning, memory, and autonomous execution.
Lead architecture reviews and establish engineering best practices for AI development.
Design reusable AI services and enterprise AI frameworks.
Agentic AI:
Design and implement multi-agent AI systems.
Architect agent orchestration, planning, reflection, memory, and tool-calling frameworks.
Build AI agents capable of executing complex business workflows autonomously.
Design human-in-the-loop systems for governance and approvals.
Optimise agent performance, reliability, observability, and cost.
LLM and Generative AI:
Architect enterprise applications using GPT, Claude, Gemini, Llama, Mistral, DeepSeek, or similar foundation models.
Design advanced RAG (Retrieval-Augmented Generation) architectures.
Build prompt engineering frameworks and prompt management strategies.
Design semantic search and knowledge retrieval systems.
Architect vector database solutions and enterprise knowledge platforms.
AI Platform Engineering:
Design scalable AI infrastructure on AWS, Azure, or GCP.
Define microservices architecture for AI workloads.
Build model serving, inference, and API gateway architecture.
Design event-driven AI systems using Kafka, Pub/Sub, or Event Hub.
Establish AI deployment pipelines with CI/CD and MLOps practices.
Leadership:
Lead AI architects and engineering teams.
Mentor senior engineers on AI architecture and engineering standards.
Collaborate with product leadership to translate business problems into AI solutions.
Drive architecture governance, design reviews, and technology decisions.
Evaluate emerging AI technologies and recommend adoption strategies.
Requirements:
12+ years of software engineering experience.
6+ years in AI/ML architecture or enterprise AI solution design.
Hands-on experience building production-grade Generative AI applications.
Experience architecting Agentic AI or autonomous AI platforms.
Strong background in distributed systems and cloud-native architectures.
Experience leading architecture for enterprise-scale applications.
Experience working with Fortune 500 companies or high-growth product organisations is preferred.
AI and Machine Learning: Large Language Models (LLMs), Generative AI, Agentic AI, Multi-Agent Systems, AI Agents, Autonomous AI Workflows, Prompt Engineering, Fine-Tuning, RAG Architecture, Embeddings, Semantic Search, Vector Databases.
AI Frameworks: LangChain, LangGraph, AutoGen, CrewAI, LlamaIndex, Semantic Kernel, MCP (Model Context Protocol), AI SDKs and orchestration frameworks
Programming: Python (Expert), FastAPI, REST APIs, Async Programming, SQL, GraphQL.
Cloud: AWS, Azure, GCP.
Databases: PostgreSQL, MongoDB, Redis, Pinecone, Weaviate, Milvus, ChromaDB, Elasticsearch/OpenSearch.
Architecture: Distributed Systems, Event-Driven Architecture, Microservices, API Gateway, Kubernetes, Docker, Terraform, Infrastructure as Code.
DevOps / MLOps: GitHub Actions, Jenkins, ArgoCD, MLflow, Kubeflow, Monitoring and Observability, Prompt Versioning, AI Evaluation Frameworks.

Experience
11-15 yrs