Senior Software Engineer - AI/ML
Bristol Myers Squibb (company site, opens in a new tab)4+ yrsHyderabadOn-siteDirect career pageEst. 18–32 LPA
Posted 1d ago
Reach people at Bristol Myers Squibb
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Bristol Myers Squibb you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Design, build, and deploy machine learning and generative AI solutions supporting key business priorities, including enterprise LLM applications and agentic AI systems.
- Collaborate with IT and business stakeholders to gather requirements, translate to user stories, and deliver production-ready Python code following software engineering best practices.
- Contribute to ML lifecycle including data management, model training, evaluation, and continuous improvement; participate in system architecture and POC development.
Minimum Requirements
- Bachelor's degree in Computer Science, Data Science, Engineering or related field (or equivalent practical experience).
- Minimum 4+ years of experience in machine learning and/or AI engineering roles, including both traditional ML and generative AI techniques.
- Proven experience with Python programming, Agile/Scrum methodologies, and collaboration tools such as JIRA and Confluence.
- Work Experience Required: 4+ years in ML/AI roles. Notice period: Not explicitly mentioned in the JD.
Ideal Candidate Profile
- Experienced in designing and delivering enterprise-grade generative AI applications using frameworks like LangChain, agent orchestration tools, and LLM platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI).
- Skilled in traditional ML tooling such as TensorFlow/Keras, Scikit-learn, and ML lifecycle tools like MLflow, with exposure to data pipelines, storage, and governance.
- Strong communicator with ability to translate complex AI/ML requirements into actionable solutions for diverse stakeholders in Agile environments.