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.