Senior Machine Learning Engineer
Eli Lilly and CompanyMatch Score
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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 employer, metro Bengaluru, mid-level ML role, and broad MLOps skillset raise candidate competition.
Core ML engineering and MLOps skills are broadly transferable, though pharmaceutical domain experience is preferred.
Explicit 4–8 years requirement plus mandatory MLOps, cloud, and CI/CD skills make screening strict.
Job Description
Structured overview of role & requirementsAbout This Role
Build and maintain production-grade ML/AI components and services with minimal supervision, including end-to-end ownership of small features or workstreams from design through deployment.
Develop and maintain CI/CD pipelines (e.g., GitHub Actions) and support deployment, monitoring, and retraining of ML models, managing issues like model drift or degradation.
Utilize tools including Docker, Kubernetes, Prefect, AWS cloud services, Databricks/Unity Catalog, and apply software engineering best practices like testing and version control in production ML/AI contexts.
Minimum Requirements
4–8 years of hands-on experience building and operating ML/AI systems in production environments.
Strong proficiency in Python with experience writing clean, testable, production-quality code.
Demonstrated experience with CI/CD pipelines, containerization (Docker), orchestration (Kubernetes), and cloud-based ML/AI systems deployment, specifically AWS and Databricks or equivalents.
Bachelor's or Master's degree in Computer Science, Computer Applications, or a related technical field.
Ideal Candidate Profile
Experienced in independently applying advanced ML engineering techniques with the ability to make technical decisions within a defined scope and manage moderately complex problems.
Proficient in production MLOps practices including containerization, orchestration, and cloud services, suitable for roles requiring hands-on engineering and platform reliability.
Familiarity with Agile/Scrum methodologies and able to lead small projects or features from design to deployment.