Job Location : Bangalore

Experience : 8 Yr

CTC Budget : 3800000 to 3800000

Posted At : 17-Dec-2025

---

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:

•        7+ years in machine learning 

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

•        5+ Flask or FastAPI or Django 

•        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.