Machine Learning Engineer

Job Summary

The Senior Machine Learning Software Engineer is a senior-level technical contributor responsible for leading the development of software infrastructure, tools, and platforms that enable scalable and maintainable machine learning operations. This role plays a critical part in bridging the gap between research and production by architecture reliable systems for training, testing, deployment, and monitoring of machine learning models. The Senior Machine Learning Software Engineer ensures AI capabilities are production-grade, reliable, and scalableunlocking innovation across all AI-driven products. In addition to making significant technical contributions, the Senior MLSE provides mentorship to junior engineers and fosters best practices in software quality, MLOps, and automation across the machine learning lifecycle.

Location: Bengaluru / Chennai / Pune

Responsibilities

• Infrastructure Design Development - Architect, build, and maintain reusable components and tools to support model training, evaluation, and deployment at scale.
• Infrastructure Design Development - Optimize model serving frameworks, feature stores, data pipelines, and CI/CD systems for ML workflows.
• Infrastructure Design Development - Ensure reliability, observability, and performance across ML systems in production.
• Technical Leadership Execution - Lead cross-functional engineering initiatives involving platform stability, experimentation infrastructure, or real-time inference systems.
• Technical Leadership Execution - Review code, propose architectural improvements, and uphold software engineering best practices within the ML engineering team.
• Technical Leadership Execution - Drive design and implementation of MLOps pipelines, automation, and model governance workflows.
• Collaboration with Research Product Engineering - Work closely with ML researchers to produce experimental models, ensuring compatibility with existing infrastructure.
• Collaboration with Research Product Engineering - Coordinate with data engineering to integrate pipelines, data validations, and model input/output schemas.
• Collaboration with Research Product Engineering - Contribute to product engineering discussions when ML systems require edge optimization, user facing API integrations, or UI-linked inference.
• Mentorship Knowledge Sharing - Mentor ML Software Engineers I and II, with a proven track record of advancing at least one MLSE I to MLSE II.
• Mentorship Knowledge Sharing - Contribute to internal documentation, architecture reviews, and engineering learning resources.
• Mentorship Knowledge Sharing - Set high standards for code quality, reproducibility, and maintainability across the ML engineering discipline.

Requirements

• 5-6 years of industry experience in ML engineering, backend engineering, or infrastructure roles supporting machine learning
• Proficient in Python and one or more systems-level languages (e.g., Go, Java, C++)
• Experience building and maintaining ML infrastructure (e.g., model registries, training orchestration, distributed data pipelines)
• Familiarity with containerization and deployment technologies (Docker, Kubernetes, AWS SageMaker, Vertex AI, etc.)
• Hands-on experience with modern MLOps frameworks (e.g., MLflow, Meta-flow, TFX, Kuberflow, etc.)
• Demonstrated mentorship experience, with direct support for the growth and promotion of junior engineer

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