Senior Manager, AI Engineer

Role Summary

Senior Manager AI Engineering to provide technical leadership for a team of AI and machine learning engineers driving the next generation of AI solutions. Shape and execute the R&D organization’s AI strategy, including end-to-end development of robust, scalable models and platforms, and ensure deployment of cutting-edge AI technologies while fostering a data-driven culture of innovation and excellence.

Responsibilities

• Lead AI Development ‚Äì Take the technical leadership of the design, build, and deployment of advanced AI/ML solutions aligned with business and scientific goals.
• Collaborate Broadly ‚Äì Partner with engineers, data scientists, and product teams to integrate models into production workflows.
• Set Standards ‚Äì Promote best practices for development, testing, deployment, and ongoing model performance.
• Drive Innovation ‚Äì Track emerging AI technologies and introduce new tools to keep the team at the forefront.
• Ensure Compliance ‚Äì Maintain strict data privacy, security, and ethical standards.

Qualifications

• Experience: 5+ years of progressive AI/ML engineering experience, including driving large-scale AI initiatives and collaboration with diverse high-performing engineering teams.
• Track Record: Proven success delivering production-grade, scalable AI/ML solutions that create measurable scientific or business impact.

Skills

• Generative AI Leadership: Deep, hands-on experience in training, fine-tuning, hosting, and deploying large-scale generative models, with mastery of PyTorch (preferred) or TensorFlow for model development and optimization.
• Agentic AI Frameworks: Demonstrated expertise in Agentic AI design and orchestration, leveraging frameworks such as LangGraph, LangChain, or similar to build intelligent, multi-step reasoning agents.
• Advanced AI Techniques: Strong capability in context engineering, prompt engineering, agent- or graph-augmented retrieval (Agent/Graph RAG), text-to-SQL generation, and multimodal AI to enable complex knowledge-driven applications.
• MLOps & Infrastructure: Robust knowledge of MLOps best practices, including CI/CD pipelines for machine learning, model versioning, automated testing, scalable deployment, and cloud platforms (AWS, Azure, GCP).

Education

• Bachelor‚Äôs or Master‚Äôs degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a closely related technical field (Ph.D. preferred).