Job Location : Bangalore

Experience : 8 Yr

CTC Budget : 10000003500000 to 10000003500000

Posted At : 15-Oct-2025

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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. 

 

Required Skills – 

 

· 7+ years of experience in machine learning, data analysis, and feature engineering with production ownership. 

· 5+ years in Python and its libraries (NumPy, pandas, scikit-learn) . 

· 4+ years in in SQL and data modeling; experience with large datasets and performance optimization. 

· 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. 

· 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.