Lead (Software Development)
•Provide day-to-day technical leadership, guidance, and task follow-up for the AI Engineering team, ensuring timely and high-quality delivery.
•Monitor project progress, technical deliverables, dependencies, and blockers, resolving issues to keep AI initiatives on track.
•Review AI solution designs, architectures, code, and implementations to ensure scalability, security, reliability, and maintainability.
•Support engineers in solving complex technical challenges and transitioning AI solutions from proof-of-concept to production.
•Drive the implementation and continuous improvement of the organization's AI Engineering Framework, standards, and reusable components.
•Guide the design and delivery of Generative AI, LLM, RAG, and Agentic AI solutions, and support broader AI adoption across Technology teams to improve productivity and efficiency.
•Promote engineering best practices, including code quality, testing, CI/CD, documentation, and monitoring, across the AI Engineering team.
•Mentor AI engineers, support knowledge sharing, and help develop the team's technical capabilities.
•Collaborate with Product, Architecture, Development, Testing, Infrastructure, and Security teams to deliver enterprise-ready AI solutions, providing regular progress and risk updates.
•Educational Background:
Bachelor's degree in Computer Science, Artificial Intelligence, Software Engineering, Data Science, or a related field.
•Experience:
Professional Experience:
6+ years of relevant experience in Software Engineering, AI, Machine Learning, or related technical areas, including 2+ years in a technical leadership, Tech Lead, or Senior Engineering role.
Management Experience:
2+ years in mentoring engineers and providing technical guidance.
Technical Skills:
•Strong understanding of Artificial Intelligence, Machine Learning, Generative AI, and Large Language Models
•Hands-on experience with LLM-based applications, Agentic AI, RAG, embeddings, vector databases, and AI orchestration
•Experience with prompt engineering, tool calling, context management, and AI evaluation
•Strong software engineering background in Python, APIs, microservices, and enterprise integrations
•Familiarity with LangGraph, LangChain, Semantic Kernel, or equivalent frameworks
•Knowledge of Docker, Kubernetes, CI/CD, Git, and cloud platforms (Azure, AWS, or GCP)
•Understanding of MLOps, LLMOps, and AI observability
•Strong awareness of AI security, governance, and responsible AI practices.