Principal Software Engineer (AI Platform) - Parametric

Morgan Stanley•Minneapolis, MN

8d•Hybrid

About The Position

The Principal Software Engineer, AI Platform, will serve as the AI engineering team lead, acting as a core technical enabler across business units. This individual will apply deep knowledge of ML, NLP, transformer architectures, and LLMs to build Parametric's GenAI platform and design, evaluate, and operationalize generative AI solutions running on this platform. This role bridges business stakeholders and engineering teams, translating business problems into technical solutions grounded in data availability and feasibility while enabling engineering teams to fully utilize the advanced AI capabilities at Morgan Stanley. Through expertise in the AI development lifecycle, from problem framing through evaluation and regression testing, combined with a platform mindset, this role ensures Parametric's GenAI solutions are robust, measurable, and continuously improving. This role will lead the engineering effort for Parametric's GenAI platform, supporting a wide range of production workloads in collaboration with internal engineering teams. The platform serves as the foundation for AI capabilities, operations, and governance within Parametric, enabling engineering teams to rapidly create safe, compliant, and highly effective GenAI-based solutions which directly solve business needs. Parametric values strong software engineering and takes pride in its culture of technical rigor, modern practices, and continuous growth. This role will help translate that culture into practical platform solutions which promote engineering effectiveness and consistent delivery.

Requirements

Bachelor’s degree in Computer Science, Machine Learning, or a related field of study; Master’s degree preferred.
10+ years of hands-on object-oriented design and development experience.
3+ years of hands-on Python software design and development.
3+ years of experience managing software development teams and providing direct guidance, mentorship, project leadership, and team development while fostering an engaging, collaborative environment and building strong cross-team relationships.
Strong communication skills, with the ability to effectively convey technical concepts to both business users and technical audiences.
Experience translating business problems into technical requirements, with the ability to assess data readiness and scope feasible AI solutions.
Demonstrated experience with the AI development lifecycle: problem framing, data assessment, model selection, prompt engineering, evaluation metrics design, testing (discriminative, regression, bias), and deployment.
Strong conceptual and practical understanding of ML, NLP, transformer architectures, and LLMs, including model architectures, training/fine-tuning paradigms, evaluation methodologies, and common failure modes.
Proficiency with LLM APIs (OpenAI, Azure OpenAI, Anthropic, open-source model serving) and frameworks (LangChain, LlamaIndex, or equivalent).
Familiarity with ML/DL frameworks: PyTorch, TensorFlow, or Hugging Face Transformers.
Proven experience building cloud-based solutions, preferably on AWS.
Experience with microservice architectures, event-driven architectures, containerization (Docker), and REST API design.
Experience with RAG architectures, vector databases, and retrieval-augmented workflows.
Experience maintaining high-quality codebases and developing and enforcing strong development standards, including Agile development practices, code reviews, and Git-based version control.
Experience using AI coding tools, such as GitHub Copilot or Claude Code.

Nice To Haves

Financial services industry experience.
Experience building libraries, low-level services, and other shared platform-level components.
Experience deploying resources using IaC technologies such as Terraform.
Experience developing CI/CD pipelines using tools like GitLab CI or GitHub Actions.
Familiarity with observability and monitoring of AI systems in production (e.g., Datadog).
Experience building evaluation and benchmarking frameworks for GenAI outputs (golden sets, human-in-the-loop review, automated scoring).
Experience presenting technical contributions and innovations to technical colleagues.

Responsibilities

Provide technical leadership by directly managing a team of engineers, guiding and mentoring them to achieve their highest potential while collectively advancing the goals and projects of the engineering team.
Drive vision and strategy for the team while exemplifying strong leadership skills, including communication, consensus building, elevated standards, ownership, accountability, and project management.
Lead GenAI development projects, including all components of the development lifecycle, from requirements gathering to deployment, bug fixes, enhancements, and escalated support.
Work directly with business users and engineering teams to build strong relationships, understand requirements, scope solutions, and communicate technical trade-offs in accessible terms.
Design, develop, and maintain high-quality and flexible platform-level technical solutions to GenAI engineering problems, including reusable components, governance processes, model evaluation, services, and APIs.
Design, develop, and maintain platform-level GenAI applications leveraging prompt engineering, RAG pipelines, fine-tuning, and agentic workflows to drive business outcomes.
Translate business problems into technical solutions by assessing data availability, feasibility, and alignment with GenAI capabilities.
Support the AI development lifecycle end-to-end: problem scoping, data assessment, solution design, implementation, evaluation, deployment, and iteration.
Design and execute AI system evaluation frameworks, including quantitative metrics definition, discriminative testing (e.g., A/B, sensitivity analysis), regression testing, and continuous quality monitoring of GenAI outputs.
Apply deep knowledge of ML, NLP, transformer architectures, and LLM internals (tokenization, attention, decoding strategies, fine-tuning paradigms) to select, adapt, and optimize foundation models (OpenAI, Llama, open source, etc.) for diverse use cases.
Conduct code and design reviews for developers working on generative AI solutions, ensuring adherence to our engineering best practices and evaluation standards.
Implement and manage cloud deployments using AWS services, ensuring high availability, scalability, and observability.
Maintain and enhance existing AI applications to improve model performance, reliability, and user experience through data-driven iteration.
Follow GenAI and related technology trends and recommend improvements to our systems when appropriate.

Benefits

Medical
Prescription Drug
Dental
Vision
Health Savings Account
Dependent Day Care Savings Account
Life Insurance
Disability and Other Insurance Plans
Paid Time Off (including Sick Leave consistent with state and local law, Parental Leave and 20 Vacation Days annually)
10 Paid Holidays
401(k)
Short/Long Term Disability