AI Technical Lead / AI Manager-(Exp-10+ Years)-Hybrid-Full time

We are seeking a highly experienced AI Technical Lead / AI Manager to provide technical leadership, architecture guidance, and delivery oversight for enterprise AI initiatives.

The successful candidate will act as a technical leader for an established team of AI/ML Engineers, Generative AI Engineers, Data Scientists, and Software Developers, providing mentorship, architectural direction, technical decision-making, and hands-on support when required.

This position requires someone who can effectively balance people leadership, AI solution architecture, hands-on engineering, stakeholder management, and delivery governance while working closely with both onsite business/technology stakeholders and distributed/offshore engineering teams.

Key Responsibilities

AI Technical Leadership

• Provide day-to-day technical leadership to AI/ML and Generative AI engineering teams.
• Mentor, coach, and guide AI engineers and developers.
• Establish AI engineering standards, development practices, and technical guidelines.
• Review technical designs, architecture documents, code, and implementation approaches.
• Act as the primary technical escalation point for complex AI-related issues.
• Drive technical decision-making across AI initiatives.
• Identify opportunities to improve engineering productivity and solution quality.

AI Solution Architecture

• Define scalable and enterprise-ready AI solution architectures.
• Translate business requirements into technical AI solution designs.
• Lead architecture and design review sessions.
• Evaluate AI technologies, frameworks, platforms, and models.
• Guide teams in selecting appropriate LLMs, AI models, APIs, frameworks, and cloud services.
• Ensure solutions meet enterprise security, scalability, reliability, and governance standards.
• Define integration patterns between AI services and existing enterprise applications.
• Design architectures for GenAI, RAG, AI agents, and intelligent automation solutions.

Generative AI & LLM

• Provide technical leadership for Generative AI and LLM-based applications.
• Design and implement solutions using commercial and open-source LLMs.
• Guide teams on prompt engineering and prompt optimization.
• Design LLM-powered applications using APIs and enterprise data.
• Evaluate model performance, accuracy, latency, and cost.
• Guide model selection and integration strategies.
• Implement LLM guardrails and responsible AI practices.
• Support LLM evaluation and regression strategies.

Agentic AI

• Lead development of AI Agent and Agentic AI solutions.
• Guide teams in designing autonomous and semi-autonomous AI workflows.
• Define agent orchestration and tool/function-calling patterns.
• Integrate AI agents with enterprise APIs, databases, applications, and external tools.
• Design multi-step AI workflows and agent-based automation.
• Guide implementation of agent memory, context management, and state handling.
• Evaluate agent performance, reliability, and task-completion quality.
• Support development of multi-agent architectures where applicable.

RAG & Enterprise AI

• Lead architecture and implementation of Retrieval-Augmented Generation (RAG) solutions.
• Guide document ingestion, chunking, embedding, indexing, and retrieval strategies.
• Design solutions using vector databases and enterprise search platforms.
• Improve retrieval relevance and response groundedness.
• Guide teams in reducing hallucinations and improving LLM response quality.
• Integrate enterprise knowledge sources with AI applications.
• Support semantic search and knowledge-retrieval use cases.

Hands-On AI Engineering

• Remain hands-on with complex technical problems and critical AI initiatives.
• Develop prototypes, proof-of-concepts, and reference implementations.
• Review Python-based AI/ML implementations.
• Support integration of AI models into enterprise applications.
• Troubleshoot performance, integration, model, and deployment issues.
• Conduct technology evaluations and proof-of-technology initiatives.
• Assist engineering teams with complex implementation challenges.

Python & AI/ML Technologies

Strong hands-on experience with:

• Python
• Pandas
• NumPy
• Scikit-learn
• PyTorch / TensorFlow
• FastAPI / Flask
• Jupyter
• REST APIs
• JSON
• SQL

Experience with AI/ML frameworks such as:

• LangChain
• LangGraph
• LlamaIndex
• Semantic Kernel
• Hugging Face
• OpenAI SDK
• Azure AI SDK
• Other enterprise AI frameworks

Cloud & AI Platforms

Experience with one or more major cloud platforms:

Microsoft Azure

• Azure OpenAI
• Azure AI Services
• Azure AI Search
• Azure Machine Learning
• Azure Functions
• Azure Container Apps / AKS

AWS

• Amazon Bedrock
• SageMaker
• Lambda
• ECS / EKS
• S3

Google Cloud

• Vertex AI
• Gemini
• BigQuery
• GKE

AI Governance & MLOps

• Guide AI solutions through development, testing, deployment, and monitoring.
• Establish AI/ML lifecycle best practices.
• Support MLOps and model deployment strategies.
• Implement model monitoring and performance tracking.
• Establish AI quality and governance standards.
• Support responsible AI practices.
• Address security, privacy, explainability, and compliance requirements.
• Establish appropriate controls around enterprise AI usage.
• Monitor model drift, quality degradation, and operational issues.