PRIMARY RESPONSIBILITES

- 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.

JOB QUALIFICATIONS

- Required Experience: 7+ years
Skill set: Python, REST APIs, LLM APIs / OpenAI, Prompt Engineering, AWS

Primary Skills

- Bachelors 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.
- Solid 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.

Good to Have Skills

- 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.

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Senior Software Engineer (GenAI) - Parametric (Mumbai)
🏢 Morgan Stanley
📍 Mumbai