As an AI Technical Lead, you will pioneer the development of groundbreaking agentic AI solutions that transform business processes and user experiences outcomes. This role combines deep technical expertise with strategic consulting to guide both internal products and clients in defining optimal AI strategies for our innovation. You will lead the exploration and implementation of diverse AI technologies—from traditional ML models to cutting-edge LLM and agent-based systems—specifically tailored for domain-specific challenges and broader applications.
Design, develop, and optimize machine learning models for various use cases including classification, regression, NLP, and computer vision.
Architect and implement state of the art agentic AI solutions, with a focus on agentic harnesses, zero trust architecture and agent evaluation and optimizations.
Hands on in the agentic system design and development lifecycle, frontend and backend are not blockers for you.
Implement MLOps and LLMOps best practices for continuous integration and deployment.
Collaborate with cross-functional teams including data engineers, product owners, and business stakeholders to define problem statements and deliver actionable solutions.
End to end understanding of RAG or GraphRAG, you can explain the why behind chunking, embedding models, metrics and different vector databases.
Ensure model or agent and model interpretability, fairness, and robustness in production environments.
Document methodologies, findings, and share knowledge through internal collaboration platforms.
Consulting and Strategy:
Client Strategy Development: Guide clients through AI strategy formulation, helping them identify optimal AI applications needs. Identify the client’s potential AI gap in their existing products or infrastructure.
Technology Assessment: Evaluate and recommend appropriate AI technologies (traditional ML, LLMs, agent systems) based on client requirements and their use cases.
Project Leadership: Lead cross-functional teams including experts and data engineers to deliver comprehensive AI solutions.
Workflow Integration: Design AI solutions that seamlessly integrate with existing the application workflow.
Compliance & Standards Leadership: Ensure all AI systems comply with relevant industry standards and best practices.
Domain Expertise Development: Collaborate closely with the client, and their experts to understand complex challenges and translate them into AI solutions.
AI Ethics and Safety: Implement explainable AI frameworks, bias mitigation strategies, and safety protocols specifically designed for applications.
Model Validation: Design and execute rigorous validation protocols for AI models in this setup, ensuring performance, quality, and adherence to standards.
Training & Coaching:
Mentor, coach, and guide team members in AI technical domains to strengthen expertise and ensure alignment with company standards. Develop technical guidelines, best practices, and reusable frameworks to drive consistency and quality across projects. Organize workshops, code reviews, and knowledge-sharing sessions to disseminate expertise in ML, LLMs, and AI systems.
Support career development of AI engineers by providing structured feedback and growth opportunities in line with organizational goals.
Ensure that training and coaching activities are aligned with the company’s long-term AI strategy and core values.
Large Language Models:
Hands-on experience with agentic implementations, including harness and context engineering
Background in working with NLP (e.g., transformers, LLMs), computer vision, or time-series data
Explainable AI: Proficiency in XAI techniques –interpretability for highly regulated applications is crucial
Strong programming skills in Python, with experience in agentic libraries such as Claude Agent SDK, Langchain, Langgraph, mem0 etc.
Comfortable with programming in TypeScript, especially with AI-related libraries such as the Vercel AI SDK, LangChain.js/LangGraph.js, and the Claude Agent SDK for TypeScript.
Hands-on experience deploying models on cloud platforms (AWS, GCP, or Azure)
Familiarity with MLOps tools such as Langfuse, Deepeval etc
Experience with Docker and Kubernetes for containerization and orchestration
Proficiency in working with APIs and integrating AI models into production systems
Master’s degree or PhD in Computer Science, Artificial Intelligence, Data Science, or a related field
Skills that will give you an advantage:
Software Development (Full stack) experience.
Computer vision and OCR experience.
Proactive, autonomous, and the ability to handle ambiguity.
Stakeholder Management: Ability to communicate complex AI concepts to client subject matter experts and non-technical stakeholders
Experience leading technical teams and client-facing consulting engagements preferred.
Experience in operating across different SDLC models — Scrum, Kanban, or hybrid setups — and able to adapt delivery cadence to the client's existing ways of working.
Excellent in English both written and verbal.
Job details are sourced from the employer's original posting.
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