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.
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