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Principal Software Engineer (AI Platform) - Parametric at Morgan Stanley Advantage Svcs · Katori

Asks for

The ad says“Design, develop, and maintain platform-level GenAI applications leveraging prompt engineering, RAG pipelines, fine-tuning, and agentic workflows to drive business outcomes.”
The ad says“Experience maintaining high-quality codebases and developing and enforcing strong development standards, including Agile development practices, code reviews, and Git-based version control.”
The ad says“Implement and manage cloud deployments using AWS services, ensuring high availability, scalability, and observability.”
The ad says“Experience using AI coding tools, such as GitHub Copilot or Claude Code.”
The ad says“Familiarity with observability and monitoring of AI systems in production (e.g., Datadog).”
The ad says“Experience with microservice architectures, event-driven architectures, containerization (Docker), and REST API design.”
The ad says“Experience with microservice architectures, event-driven architectures, containerization (Docker), and REST API design.”
The ad says“Experience maintaining high-quality codebases and developing and enforcing strong development standards, including Agile development practices, code reviews, and Git-based version control.”
The ad says“Experience developing CI/CD pipelines using tools like GitLab CI or GitHub Actions.”
The ad says“Experience using AI coding tools, such as GitHub Copilot or Claude Code.”
The ad says“Experience developing CI/CD pipelines using tools like GitLab CI or GitHub Actions.”
The ad says“Familiarity with ML/DL frameworks: PyTorch, TensorFlow, or Hugging Face Transformers.”
The ad says“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.”
The ad says“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.”
The ad says“Experience with microservice architectures, event-driven architectures, containerization (Docker), and REST API design.”
The ad says“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.”
The ad says“10+ years of hands-on object-oriented design and development experience.”
The ad says“Design, develop, and maintain platform-level GenAI applications leveraging prompt engineering, RAG pipelines, fine-tuning, and agentic workflows to drive business outcomes.”
The ad says“3+ years of hands-on Python software design and development.”
The ad says“Familiarity with ML/DL frameworks: PyTorch, TensorFlow, or Hugging Face Transformers.”
The ad says“Design, develop, and maintain platform-level GenAI applications leveraging prompt engineering, RAG pipelines, fine-tuning, and agentic workflows to drive business outcomes.”
The ad says“Experience with microservice architectures, event-driven architectures, containerization (Docker), and REST API design.”
The ad says“Familiarity with ML/DL frameworks: PyTorch, TensorFlow, or Hugging Face Transformers.”
The ad says“Experience deploying resources using IaC technologies such as Terraform.”
The ad says“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.”
The ad says“Experience with RAG architectures, vector databases, and retrieval-augmented workflows.”

ABOUT MORGAN STANLEY
Morgan Stanley is a leading global financial services firm providing a wide range of investment banking, securities, wealth management and investment management services. With offices in more than 41 countries, the Firm's employees serve clients worldwide including corporations, governments, institutions, and individuals. For further information about Morgan Stanley, please visit www.morganstanley.com.

ABOUT PARAMETRIC
Parametric is part of Morgan Stanley Investment Management, the asset management division of Morgan Stanley. We partner with advisors, institutions, and consultants to build portfolios focused on what's important to them and their clients. A leader in custom solutions for more than 30 years, we help investors access efficient market exposures, solve implementation challenges, and design multi-asset portfolios that respond to their evolving needs. We also offer systematic alpha and alternative strategies to complement clients' core holdings.

This role is part of Parametric's hybrid working model, which includes working in the office 3 days a week and choosing to work remotely or in the office the remaining days of the week.

ABOUT THE TEAM
The Core Platform and AI Engineering team at Parametric is responsible for enhancing the quality and velocity of engineering outcomes across the technology organization through platform-level capabilities, accelerating engineering teams and enabling them to focus on delivering business value.

This team delivers Parametric's platform-level service and development capabilities. These efforts seek to optimize the organization's technology posture by providing common solutions to shared problems, driving standardization, accelerated delivery, reduced risk and cost, and scalable, high-performing results. The team serves engineering teams across the organization and is responsible for supporting their success.

ABOUT THE ROLE
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 available 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.

PRIMARY 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.

JOB QUALIFICATIONS

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.

Preferred Qualifications

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.

Parametric believes each member of our organization makes a significant contribution to our success. That contribution should not be limited by the assigned responsibilities. Therefore, this job description is designed to outline primary duties and qualifications. It is our expectation that every member of our team will offer his/her/their services wherever and whenever necessary to ensure the success of our client services.

WHAT YOU CAN EXPECT FROM MORGAN STANLEY:

At Morgan Stanley, we raise, manage and allocate capital for our clients – helping them reach their goals. We do it in a way that’s differentiated – and we’ve done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren’t just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There’s also ample opportunity to move about the business for those who show passion and grit in their work.

To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices into your browser.

Salary range for the position: $115,000 - $225,000/Yr. The successful candidate may be eligible for an annual discretionary incentive compensation award. The successful candidate may be eligible to participate in the relevant business unit's incentive compensation plan, which also may include a discretionary bonus component. Morgan Stanley offers a full spectrum of benefits, including 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), and Short/Long Term Disability, in addition to other special perks reserved for our employees. Please visit mybenefits.morganstanley.com to learn more about our benefit offerings.

Morgan Stanley is an equal opportunity employer committed to building and maintaining a workforce that is diverse in experience and background. Our recruiting efforts reflect our strong commitment to a culture of inclusion, where individuals are hired, developed, and advanced based on their skills and talents.

Our workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences.

For more information, please visit: https://www.morganstanley.com/people-opportunities/eeo.