The Role
Develop and lead scalable machine learning infrastructure, including training, evaluation, deployment, model serving, feature stores, data pipelines, CI/CD, observability, and governance. Collaborate with researchers, data engineers, and product teams to productionize models and support real-time inference. The role includes architectural leadership, code reviews, automation, documentation, and mentoring junior ML engineers.
Summary Generated by Built In
Job Description – Sr. Machine Learning Software Engineer
Experience level: 5 - 8 Years
Qualification: Postgraduate/ Graduate
Location: Bengaluru /Chennai/Pune
Position 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 scalable—unlocking 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.
Responsibilities:
Infrastructure Design & Development
● Architect, build, and maintain reusable components and tools to support model training, evaluation, and deployment at scale.
● Optimize model serving frameworks, feature stores, data pipelines, and CI/CD systems for ML workflows.
● 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.
● Review code, propose architectural improvements, and uphold software engineering best practices within the ML engineering team.
● 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.
● Coordinate with data engineering to integrate pipelines, data validations, and model input/output schemas.
● 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.
● Contribute to internal documentation, architecture reviews, and engineering learning resources.
● 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
Skills Required
- Postgraduate or graduate qualification
- 5–6 years of industry experience in ML engineering, backend engineering, or infrastructure roles supporting machine learning
- Proficiency in Python
- Proficiency in one or more systems-level languages, such as Go, Java, or C++
- Experience building and maintaining machine learning infrastructure, including model registries, training orchestration, or distributed data pipelines
- Familiarity with containerization and deployment technologies such as Docker, Kubernetes, AWS SageMaker, or Vertex AI
- Hands-on experience with MLOps frameworks such as MLflow, Metaflow, TFX, or Kubeflow
- Demonstrated mentorship experience supporting the growth and promotion of junior engineers
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The Company
What We Do
Blackbuck Insights is a global data and AI consultancy providing data engineering services, cloud modernization, AI activation, and platform operations. The company helps enterprises modernize legacy environments, migrate and scale data platforms, build AI-enabled business applications, and establish trusted data foundations for analytics and intelligent decision-making. Its services support organizations seeking reliable, performant, and scalable technology solutions.
