Gen AI Lead

Role & responsibilities

• Define and implement the Generative AI strategy, technical roadmap, and architecture aligned with business objectives.
• Lead the design, development, testing, and deployment of GenAI solutions using LLMs and foundation models.
• Build and optimise RAG pipelines, AI agents, chatbots, and intelligent automation systems.
• Evaluate, select, fine-tune, and integrate LLMs for specific business use cases.
• Lead prompt engineering, model evaluation, benchmarking, and performance optimisation.
• Design scalable AI architectures using vector databases, APIs, cloud platforms, and distributed systems.
• Establish MLOps/LLMOps practices for model deployment, monitoring, versioning, and continuous improvement.
• Ensure AI solutions meet requirements for data privacy, security, reliability, responsible AI, and regulatory compliance.
• Collaborate with product, engineering, data science, and business teams to identify and deliver high-impact AI use cases.
• Manage technical project delivery, resource allocation, and stakeholder expectations.
• Mentor AI/ML engineers and establish best practices for GenAI development.
• Track emerging AI technologies and drive innovation, cost optimisation, and measurable business outcomes.

Preferred candidate profile

• Bachelor’s degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, or a related field.
• Master’s degree in AI/ML, Computer Science, or Data Science preferred but not mandatory.
• 5 to 10 years of experience in software engineering, machine learning, AI, or data science, with hands-on Generative AI implementation experience.
• Strong programming skills in Python and experience with frameworks such as PyTorch, TensorFlow, LangChain, or LangGraph.
• Practical expertise in LLMs, RAG, embeddings, vector databases, prompt engineering, fine-tuning, and AI agents.
• Experience deploying production-grade AI applications using AWS, Azure, or Google Cloud Platform.
• Knowledge of Docker, Kubernetes, CI/CD, MLOps/LLMOps, model monitoring, and scalable system architecture.
• Strong understanding of AI evaluation, hallucination mitigation, guardrails, data security, and responsible AI practices.
• Proven ability to lead technical teams, manage projects, and collaborate across cross-functional teams.
• Excellent analytical, problem-solving, communication, and stakeholder management skills.
• Ability to translate business requirements into secure, reliable, cost-effective, and scalable GenAI solutions.