AI Technical Lead

Technical Leadership

• Define and review AI solution architectures and implementation approaches.
• Guide engineering teams on best practices for AI application development.
• Identify technical risks, implementation gaps, and optimization opportunities.
• Ensure AI solutions meet performance, scalability, security, and maintainability requirements.

Hands-on Development

• Design and develop AI-powered applications and workflows.
• Build and optimize Retrieval-Augmented Generation (RAG) solutions.
• Develop AI agents, orchestration workflows, and automation solutions.
• Integrate LLMs with enterprise applications, APIs, databases, and third-party systems.
• Support deployment, testing, monitoring, and troubleshooting of AI solutions in production environments.

Mentoring & Team Enablement

• Provide technical mentorship to developers and junior AI engineers.
• Conduct code reviews and architecture reviews.
• Establish development standards, reusable frameworks, and implementation guidelines.
• Help teams translate business requirements into effective AI solutions.

Governance & Quality

• Define evaluation frameworks and success metrics for AI solutions.
• Implement observability, monitoring, and performance tracking.
• Ensure compliance with security, privacy, and responsible AI practices.
• Drive continuous improvement through experimentation and adoption of emerging AI technologies.

We are seeking an experienced AI Technical Lead to drive the design, development, and implementation of enterprise-grade AI solutions. The ideal candidate will possess strong hands-on experience building and deploying AI applications in production environments and will provide both technical leadership and execution support to development teams.

This role requires a balance of solution architecture, hands-on development, mentoring, and delivery ownership to ensure AI initiatives are scalable, secure, reliable, and aligned with business objectives.

7-12 Years (with minimum 3+ years in AI/ML and Generative AI solution development), * Strong experience working with OpenAI, Anthropic Claude, Gemini, or similar LLMs.

• Expertise in prompt engineering and prompt optimization.
• Experience building RAG-based applications.
• Knowledge of AI agent frameworks and orchestration patterns.
• Understanding of model evaluation, grounding, hallucination mitigation, and AI quality assurance.

AI Engineering

• Experience with LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or similar frameworks.
• Experience integrating vector databases such as Pinecone, Weaviate, Chroma, or Qdrant.
• Knowledge of embeddings, semantic search, and retrieval techniques.
• Experience implementing AI workflows and automation solutions., * Strong programming skills in Python.
• Experience with REST APIs and microservice architectures.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Understanding of CI/CD, deployment pipelines, and DevOps practices.
• Experience working with SQL and NoSQL databases.

Production Experience

• Proven experience delivering AI solutions to production environments.
• Experience monitoring and optimizing AI application performance.
• Strong troubleshooting and debugging capabilities for AI systems at scale., * Experience leading AI teams or mentoring engineers.
• Experience building enterprise AI assistants, copilots, chatbots, or workflow automation solutions.
• Familiarity with MLOps and AI governance practices.
• Exposure to manufacturing, ERP, inventory, or field service domains is a plus.

Key Success Criteria

• Successful delivery of production-ready AI solutions.
• Improved development velocity through technical leadership and mentoring.
• Reduced implementation rework and technical debt.
• Establishment of scalable AI engineering practices and standards.
• High-quality, maintainable, and secure AI implementations.