As an Applied AI ML Lead in the Chief Data & Analytics Office, you will lead the development and deployment of innovative AI and machine learning solutions. You will collaborate with cross-functional teams to address complex business challenges, drive adoption of modern ML practices, and ensure responsible AI governance. You will have the opportunity to work withstate-of-the-arttechnologies and contribute to a culture of technical excellence and continuous learning.

Job Responsibilities:

• Lead the hands-on design, development, and deployment of advanced AI, GenAI, and large language model solutions.
• Serve as a subject matter expert on a wide range of machine learning techniques and optimizations.
• Collaborate with product, engineering, and business teams to deliver scalable, production-ready AI systems.
• Conduct experiments using the latest ML technologies, analyze results, and tune models foroptimalperformance.
• Own end-to-end code development in Python for both proof-of-concept and production-ready solutions.
• Integrate generative AI within the ML platform usingstate-of-the-arttechniques.
• Drive adoption of modern ML infrastructure, tools, and best practices.
• Optimizesystem accuracy and performance byidentifyingand resolving inefficiencies.
• Communicate technical concepts and results to both technical and business stakeholders.
• Ensure responsible AI practices, model governance, and compliance with regulatory standards.
• Mentor and guide other AI engineers and scientists, fostering a culture of continuous learning.

Required Qualifications, Capabilities, and Skills:

• Masters or PhD in Computer Science, Engineering, Mathematics, or a related quantitative field.
• Minimum 8 years of hands-on experience in applied machine learning, including generative AI, large language models, or foundation models.
• At least 5 years of experience programming in Python; experience with ML frameworks such asPyTorchor TensorFlow.
• Proven experience designing, training, and deploying large-scale ML/AI models in production environments.
• Deep understanding of prompt engineering, agentic workflows, and orchestration frameworks.
• Experience with cloud platforms (AWS, Azure, GCP) and distributed systems (Kubernetes, Ray,Slurm).
• Solid grasp ofMLOpstools and practices (MLflow, model monitoring, CI/CD for ML).
• Strong communicationskills with the ability to explain complex technical concepts to diverse audiences.
• Demonstrated leadership in working effectively with engineers, product managers, and other ML practitioners.
• Experience applying data science and ML techniques to solve business problems.
• Passion for detail, follow-through, and technical excellence.

Preferred Qualifications, Capabilities, and Skills:

• Experience with high-performance computing and GPU infrastructure (e.g., NVIDIA DCGM, Triton Inference).
• Familiarity with big data processing tools and cloud data services.
• Advanced knowledge in reinforcement learning, meta learning, or related advanced ML areas.
• Experience with search/ranking, recommender systems, or graph techniques.
• Background in financial services or regulated industries.
• Experience withbuilding and deploying ML models on cloud platforms such as AWSSagemaker, EKS, etc.
• Published research or contributions to open-source GenAI/LLM projects.

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