AI Technical Lead

Role Overview

We are looking for a hands-on Technical Lead AI Engineering to lead AI engineers and develop, deploy, and maintain production-grade AI solutions. The role requires strong technical expertise, coding proficiency, architecture design, code reviews, team mentoring, and end-to-end ownership of AI application delivery.

The ideal candidate should have practical experience with Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and AI orchestration frameworks, along with strong software engineering and deployment skills.

Key Responsibilities

• Lead AI engineering projects from requirement analysis and solution design through development, testing, and production deployment.
• Design and develop scalable AI applications, APIs, microservices, and reusable software components.
• Build AI solutions using GenAI, LLMs, RAG, Agentic AI, Model Context Protocol (MCP), Computer Vision, Voice AI, and Document Intelligence.
• Define technical architecture, engineering standards, and development best practices.
• Plan and track engineering activities, manage technical dependencies, and resolve development blockers.
• Conduct code reviews and ensure code quality, performance, security, and maintainability.
• Mentor AI engineers and support technical decision-making and knowledge sharing.
• Collaborate with cross-functional teams and stakeholders to ensure timely delivery of projects.
• Support sprint planning, testing, release management, deployment, and production troubleshooting.
• Ensure AI applications are scalable, reliable, secure, observable, and cost-efficient.
• Drive the adoption of reusable frameworks, tools, and modern AI engineering practices.

Required Skills and Qualifications

• 6+ years of experience in Software Engineering, AI/ML Engineering, GenAI, or AI Platform Development.
• Proven experience building and deploying production-grade AI applications.
• Strong hands-on experience with Python, REST APIs, microservices, SQL, and distributed systems.
• Strong understanding of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, MCP, and AI orchestration.
• Experience with frameworks such as LangChain, LangGraph, Semantic Kernel, or equivalent AI development frameworks.
• Experience designing and developing scalable backend services and AI-powered applications.
• Familiarity with Docker, Kubernetes, CI/CD pipelines, and cloud-based deployment.
• Understanding of MLflow, model lifecycle management, and application observability tools.
• Experience leading engineering teams, conducting code reviews, and mentoring developers.
• Strong problem-solving, communication, technical decision-making, and stakeholder-management skills.

Preferred Qualifications

• Experience leading the end-to-end delivery of enterprise-grade AI solutions.
• Knowledge of prompt engineering, LLM evaluation, AI guardrails, and responsible AI practices.
• Experience with MLOps/LLMOps, model monitoring, version control, and automated deployment.
• Understanding of AI security, data privacy, governance, and regulatory compliance.
• Experience optimizing AI applications for accuracy, latency, scalability, and operational cost.
• Ability to evaluate emerging AI technologies and translate them into practical business solutions.
• Experience establishing reusable frameworks, engineering standards, and best practices.