Lead Agentic AI Engineer
Job Description
We are looking for a "Lead Agentic AI Engineer" for our client to drive the design, development, and deployment of advanced AI-powered applications using Generative AI and agentic frameworks.
This role combines deep technical expertise with leadership responsibilities, including guiding architecture, mentoring engineers, and shaping AI strategy across initiatives.
Key Responsibilities
• Lead the design and development of scalable backend systems using Python and FastAPI. • Architect and deliver end-to-end GenAI and Agentic AI applications in production. • Partner with Architects to define technical strategy and best practices for agentic AI systems. • Integrate AI solutions with enterprise data platforms, including SQL and Databricks. • Provide technical leadership, mentorship, and conduct code reviews. • Collaborate with stakeholders to translate business needs into AI-driven solutions. • Oversee deployment, scalability, and performance on cloud platforms, such as AWS or Azure. • Drive the adoption of emerging frameworks, including AWS AgentCore, as part of the Phase 2 roadmap.
Required Skills (Must-Have)
• Expert-level backend development experience with Python and FastAPI. • Strong experience with SQL, Databricks, and relational databases. • Hands-on experience with at least one cloud platform, such as AWS or Azure. • Proven track record of building and scaling end-to-end GenAI or Agentic AI applications. • Experience leading technical teams or owning architecture for complex systems.
Preferred Skills (Good-to-Have)
• Frontend experience with React and Tailwind CSS. • Familiarity with AWS AgentCore or similar agent orchestration frameworks.
What We’re Looking For
• Strong leadership and decision-making skills. • Ability to balance hands-on development with strategic oversight. • Excellent communication skills with both technical and business stakeholders.
What you'll do
- Design, train, and evaluate large language models and agentic pipelines
- Build production-grade inference and orchestration systems
- Collaborate with product and engineering teams to ship AI features end-to-end
- Run experiments, benchmark models, and iterate on prompt and fine-tuning strategies
- Own model quality, latency, and cost across the stack
- Mentor engineers and contribute to internal ML platform improvements
What we're looking for
- Strong background in machine learning, NLP, or deep learning
- Hands-on experience with PyTorch, Hugging Face, or similar frameworks
- Experience deploying models to production
- Solid Python engineering skills
- Familiarity with RAG, vector databases, and agent frameworks
- Strong communication skills in English
Nice to have
- Published research or open-source contributions
- Experience with distributed training or GPU optimization