Senior Software Engineer (GenAI) - Parametric

Overview

Senior Software Engineer (Generative AI) at Parametric, part of Morgan Stanley Investment Management, serving as a core technical enabler across business units by designing, evaluating, and operationalizing generative AI solutions within the Core Platform and AI Engineering team.

What you'll do

• Translate business problems into technical solutions by assessing data availability, feasibility, and alignment with GenAI capabilities.
• Own and drive 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.
• Design, develop, and maintain platform-level GenAI applications leveraging prompt engineering, RAG pipelines, fine-tuning, and agentic workflows to drive business outcomes.
• Interact directly with business users and engineering teams to understand requirements, scope solutions, and communicate technical trade-offs in accessible terms.
• Conduct code and design reviews for developers working on generative AI solutions, ensuring adherence to engineering best practices and evaluation standards.
• Implement and manage cloud deployments using AWS service, ensuring high availability, scalability, and observability.
• Maintain and enhance existing AI applications to improve model performance, reliability, and user experience through data-driven iteration.

What you'll need

• Required experience: 7+ years.
• Bachelor's degree in Computer Science, Machine Learning, Data Science, or a related field of study.
• 5+ years of hands-on Python software design and development.
• Strong communication skills, with the ability to effectively convey technical concepts to both business users and technical audiences alike.
• Strong conceptual and practical understanding of ML, NLP, Transformers, and LLMs, including model architectures, training/fine-tuning paradigms, evaluation methodologies, and common failure modes.
• Demonstrated experience with the AI development lifecycle: problem framing, data assessment, model selection, prompt engineering, evaluation metrics design, testing (discriminative, regression, bias), and deployment.
• Experience translating business problems into technical requirements, with the ability to assess data readiness and scope feasible AI solutions.
• Proficiency with LLM APIs (OpenAI, Azure OpenAI, Anthropic, open-source model serving) and frameworks (LangChain, LlamaIndex, or equivalent).
• Hands-on experience building cloud-based solutions, preferably on AWS.
• Familiarity with ML/DL frameworks: PyTorch, TensorFlow, or Hugging Face Transformers.
• Familiarity with microservices architecture, containerization (Docker), and REST API design.
• Working experience with Agile development and Git version control.
• Knowledge of RAG architectures, vector databases, and retrieval-augmented workflows.

Nice to have

• Financial services industry experience.
• Cloud-native development experience (AWS preferred).
• 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.

Details

• Location: Mumbai, India.
• Work mode: Hybrid, working in the office 3 days a week and choosing to work remotely or in the office the remaining days.
• Team members are expected to offer services wherever and whenever necessary to ensure the success of client services.