Description :

Role Senior Machine Learning Engineer

Experience : 8+ years

Location : Bengaluru (Hybrid)

Responsibilities :

- Design, develop, and deploy machine learning models and algorithms for production use with clear SLAs.

- Build and maintain scalable, reliable data pipelines (batch and streaming) for training and inference.

- Perform exploratory data analysis to uncover insights, define hypotheses, and guide feature design.

- Develop robust feature engineering processes; manage feature definitions, lineage, and reuse across teams.

- Implement model serving as APIs/services (REST/gRPC) using Flask/FastAPI/Django with proper versioning and rollback.

- Establish CI/CD for ML (testing, packaging, model artifacts) with automated deployments and canary/blue-green strategies.

- Set up experiment tracking, model registry, and reproducible training workflows.

- Define and monitor offline/online metrics.

- Implement observability across data, models, and services (latency, throughput, drift, data quality, cost).

- Collaborate with product, data, and platform teams to translate requirements into technical designs and roadmaps.

- Write clear documentation and participate in code reviews and mentoring.

- Participate in incident response and on-call rotations for ML services.

Requirements :

- Minimum of 8 years of experience in machine learning, data analysis, and feature engineering with production ownership.

- Strong proficiency in Python and its libraries (NumPy, pandas, scikit-learn) .

- Experience with one or more web frameworks such as Flask, FastAPI, or Django to build production-grade APIs.

- Solid understanding of ML algorithms, evaluation techniques, experiment design, and statistical testing.

- Proficiency in SQL and data modeling; experience with large datasets and performance optimization.

- Hands-on experience with data processing frameworks (e.g., Spark/Beam/Flink) and streaming platforms (e.g., Kafka/Kinesis).

- Strong software engineering skills: modular design, type hints, unit/integration testing (pytest), logging, and profiling.

- Experience with containers and orchestration (Docker, Kubernetes) and infrastructure-as-code concepts.

- Familiarity with CI/CD tools (e.g., GitHub Actions/GitLab/Jenkins) for automating ML builds and releases.

- Monitoring/observability experience (e.g., Prometheus/Grafana/OpenTelemetry) and data quality checks/drift detection.

- Excellent communication skills to effectively convey technical concepts to non-technical stakeholders and drive alignment.

Good to have :

- Experience with cloud platforms (AWS preferred) and common services (e.g., S3, ECR, ECS/EKS, Lambda/Batch, IAM).

- Experience with MLOps practices and tools (feature stores, data/version management like DVC/LakeFS, workflow orchestration like Airflow/Dagster).

- Experience with Natural Language Processing (NLP), computer vision, recommendation/ranking, or time-series forecasting.

- Familiarity with dashboarding/visualization for analysis and monitoring (Matplotlib, Plotly, Grafana, Streamlit).