Senior Machine Learning Engineer

Senior Machine Learning Engineer Job Description

Position: Senior Machine Learning Engineer

Experience: 7+ years

Location: Bengaluru (Hybrid)

Skills: Python, Machine Learning, Flask/FastAPI/Django, SQL, Data Pipelines, MLOps

Role Summary

The Senior Machine Learning Engineer will design, build, and productionize machine learning solutions end to end, from data pipelines and model development to scalable deployment and monitoring. The role combines strong software engineering fundamentals with deep ML expertise and ownership of production systems, including reliability, performance, and cost.

Key Responsibilities

Machine Learning & Modeling

• Design, develop, and deploy machine learning models and algorithms for production use with clear SLAs and business impact.
• Perform exploratory data analysis to uncover insights, define hypotheses, and guide feature and model design.
• Develop robust feature engineering workflows; manage feature definitions, lineage, and reuse across teams and projects.
• Define and monitor offline and online metrics, ensuring rigorous evaluation, experiment design, and statistical testing.

Data Engineering & Pipelines

• Build and maintain scalable, reliable batch and streaming data pipelines for training and inference.
• Work with large, complex datasets using SQL and data processing frameworks (Spark/Beam/Flink) and streaming platforms (Kafka/Kinesis).
• Ensure data quality, integrity, and consistency, including proactive monitoring for data drift and anomalies.

MLOps, Deployment & Observability

• Implement model serving as APIs/services (REST/gRPC) using Flask/FastAPI/Django with proper versioning and rollback strategies.
• Establish CI/CD for ML: automated testing, packaging, model artifact management, and safe deployment (canary/bluegreen).
• Set up experiment tracking, model registry, and reproducible training workflows.
• Implement observability across data, models, and services (latency, throughput, drift, data quality, and cost).
• Participate in incident response and oncall rotations for ML services; troubleshoot and resolve production issues.

Collaboration, Leadership & Governance

• Collaborate with product, data, and platform teams to translate business requirements into technical designs, roadmaps, and detailed user stories.
• Mentor and guide junior engineers; set and review FAST goals (Frequently discussed, Ambitious, Specific, Transparent) for self and team.
• Conduct and participate in code reviews, design reviews, and technical discussions to maintain high engineering standards.
• Communicate complex technical topics clearly to nontechnical stakeholders, including through presentations and customer calls.

Required Qualifications & Skills

Technical Skills

• Minimum 7 years of handson experience in machine learning, data analysis, and feature engineering with production ownership.
• Strong proficiency in Python and core libraries (NumPy, pandas, scikitlearn); exposure to deep learning frameworks is a plus.
• Experience with at least one web framework such as Flask, FastAPI, or Django to build productiongrade APIs.
• Solid understanding of ML algorithms, evaluation techniques, experiment design, and statistical testing.
• Strong software engineering skills: modular design, type hints, unit/integration testing (e.g., pytest), logging, and profiling.
• Proficiency in SQL and data modeling; experience with performance optimization on large datasets.
• Handson experience with data processing frameworks (Spark/Beam/Flink) and streaming platforms (Kafka/Kinesis).
• Experience with containers and orchestration (Docker, Kubernetes) and infrastructureascode concepts.
• Familiarity with CI/CD tools (GitHub Actions, GitLab CI, Jenkins, etc.) for automating ML builds and releases.
• Monitoring/observability experience (Prometheus, Grafana, OpenTelemetry) and implementing data quality checks/drift detection.

Nice to Have

• Experience with cloud platforms (AWS preferred) and services such as S3, ECR, ECS/EKS, Lambda/Batch, and IAM.
• Experience with MLOps tools and practices: feature stores, data/model versioning (DVC, LakeFS), workflow orchestrators (Airflow, Dagster, etc.).
• Experience in one or more domains: NLP, computer vision, recommendation/ranking systems, or timeseries forecasting.
• Familiarity with dashboarding and visualization tools (Matplotlib, Plotly, Grafana, Streamlit) for analysis and monitoring.

Behavioral & Leadership Competencies

• Strong analytical and problemsolving skills with the ability to break down complex problems into logical components.
• Ability to work under pressure, handle multiple tasks, and manage dependencies and risks effectively.
• Excellent verbal and written communication; high standard of business etiquette in emails, documentation, and customer interactions.
• Proven ability to build trust with stakeholders by delivering highquality solutions on time and with measurable value.
• Proactive, collaborative mindset: asks for and offers help, drives alignment across teams, and maintains high team motivation and engagement.

Education & Certifications

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
• Relevant certifications in cloud, data engineering, or MLOps/ML are an added advantage.