Lead I

5 days ago

Yelahanka, Karnataka, India ANRGI TECH Pvt. Ltd. Full time ₹ 12,00,000 - ₹ 36,00,000 per year
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 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).


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