Note: Fidelity will not provide immigration sponsorship for this position.

Position Description:

Leads the development and delivery of advanced Artificial Intelligence (AI) solutions for financial services, with a particular focus on Large Language Model (LLM) powered applications. Provides strategic and technical leadership in designing, training, fine-tuning, and deploying transformer-based models and Agentic AI systems. Works within an innovation-driven incubator environment to guide end-to-end product development, from ideation and experimentation through production-ready implementation, using scalable Machine Learning (ML) infrastructure, distributed training systems, and modern Large Language Model Operations (LLMOps) practices. Designs and implements scalable ML workflows using Python, PyTorch, and related frameworks to support production grade systems. Drives alignment with engineering, product, and business stakeholders to advance AI capabilities, accelerate experimentation, and deliver high-impact solutions that support new business growth across the financial services ecosystem.

Primary Responsibilities:

• Oversees LLM post training activities -- fine tuning, alignment, optimization, and performance evaluation.

• Architects and optimizes distributed ML training and inference systems to enhance performance, memory efficiency, and latency.

• Manages model hosting, serving, and deployment pipelines using containerization technologies and model serving frameworks within cloud environments.

• Applies Agentic AI technologies, prompt-engineering methods, and work-flow orchestration tools to develop advanced AI driven applications.

• Develops synthetic data pipelines, evaluation methodologies, and custom loss functions to strengthen model robustness and accuracy.

• Guides cross-functional teams in delivering AI-enabled features and products, ensuring scalability, reliability, and compliance.

• Partners with product and business leaders to translate conceptual ideas into validated prototypes and production-ready solutions.

• Establishes and maintains LLMOps and Machine Learning Operations (MLOps) best practices, including monitoring, versioning, and continuous optimization of AI systems.

Education and Experience:

Bachelor’s degree in Computer Science, Engineering, Information Technology, Information Systems, Physics, or a closely related field (or foreign education equivalent) and six (6) years of experience as a Director, Data Science (or closely related occupation) developing end-to-end AI and ML solutions, including LLM-based and Agentic AI systems, using Python, PyTorch, and cloud-based MLOps and LLMOps technologies within a financial services environment.

Or, alternatively, Master’s degree in Computer Science, Engineering, Information Technology, Information Systems, Physics, or a closely related field (or foreign education equivalent) and four (4) years of experience as a Director, Data Science (or closely related occupation) developing end-to-end AI and ML solutions, including LLM-based and Agentic AI systems, using Python, PyTorch, and cloud-based MLOps and LLMOps technologies within a financial services environment.

Or, alternatively, PhD in Computer Science, Engineering, Information Technology, Information Systems, Physics, or a closely related field (or foreign education equivalent) and one (1) year of experience as a Director, Data Science (or closely related occupation) developing end-to-end AI and ML solutions, including LLM-based and Agentic AI systems, using Python, PyTorch, and cloud-based MLOps and LLMOps technologies within a financial services environment.

Skills and Knowledge:

Candidate must also possess:

• Demonstrated Expertise (“DE”) designing and orchestrating scalable ML workflows using Python, PyTorch, Transformers, Compute Unified Device Architecture (CUDA), ML algorithms, and workflow automation frameworks within a production grade AI development environment.

• DE architecting and optimizing distributed training and inference systems using parallel computed frameworks vLLM and leveraging performance-engineering, and profiling tools including PyTorch Profiler and Nvidia Nsight Systems within latency‑sensitive AI environments.

• DE implementing model deployment, hosting, and serving using containerization technologies, model serving frameworks, and cloud infrastructure services (Amazon Elastic Kubernetes Service (EKS)) within a scalable enterprise production environment.

• DE advancing AI-driven application development using Agentic AI technologies (Agentic frameworks LangGraph and Agent Developer Kit (ADK)), prompt and context engineering methodologies, synthetic data pipelines, and evaluation frameworks within an Agile innovation and product development environment.

Salary: $183,000.00 to $212,000.00/year.

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