Job Description
<p><p><b>Description :</b><br/><br/>Role Senior Machine Learning Engineer<br/><br/>Experience : 8+ years<br/><br/>Location : Bengaluru (Hybrid)<br/><br/><b>Responsibilities :</b><br/><br/>- Design, develop, and deploy machine learning models and algorithms for production use with clear SLAs.<br/><br/>- Build and maintain scalable, reliable data pipelines (batch and streaming) for training and inference.<br/><br/>- Perform exploratory data analysis to uncover insights, define hypotheses, and guide feature design.<br/><br/>- Develop robust feature engineering processes; manage feature definitions, lineage, and reuse across teams.<br/><br/>- Implement model serving as APIs/services (REST/gRPC) using Flask/FastAPI/Django with proper versioning and rollback.<br/><br/>- Establish CI/CD for ML (testing, packaging, model artifacts) with automated deployments and canary/blue-green strategies.<br/><br/>- Set up experiment tracking, model registry, and reproducible training workflows.<br/><br/>- Define and monitor offline/online metrics.<br/><br/>- Implement observability across data, models, and services (latency, throughput, drift, data quality, cost).<br/><br/>- Collaborate with product, data, and platform teams to translate requirements into technical designs and roadmaps.<br/><br/>- Write clear documentation and participate in code reviews and mentoring.<br/><br/>- Participate in incident response and on-call rotations for ML services.<br/><br/><b>Requirements :</b><br/><br/>- Minimum of 8 years of experience in machine learning, data analysis, and feature engineering with production ownership.<br/><br/>- Strong proficiency in Python and its libraries (NumPy, pandas, scikit-learn) .<br/><br/>- Experience with one or more web frameworks such as Flask, FastAPI, or Django to build production-grade APIs.<br/><br/>- Solid understanding of ML algorithms, evaluation techniques, experiment design, and statistical testing.<br/><br/>- Proficiency in SQL and data modeling; experience with large datasets and performance optimization.<br/><br/>- Hands-on experience with data processing frameworks (e.g., Spark/Beam/Flink) and streaming platforms (e.g., Kafka/Kinesis).<br/><br/>- Strong software engineering skills: modular design, type hints, unit/integration testing (pytest), logging, and profiling.<br/><br/>- Experience with containers and orchestration (Docker, Kubernetes) and infrastructure-as-code concepts.<br/><br/>- Familiarity with CI/CD tools (e.g., GitHub Actions/GitLab/Jenkins) for automating ML builds and releases.<br/><br/>- Monitoring/observability experience (e.g., Prometheus/Grafana/OpenTelemetry) and data quality checks/drift detection.<br/><br/>- Excellent communication skills to effectively convey technical concepts to non-technical stakeholders and drive alignment.<br/><br/><b>Good to have :</b><br/><br/>- Experience with cloud platforms (AWS preferred) and common services (e.g., S3, ECR, ECS/EKS, Lambda/Batch, IAM).<br/><br/>- Experience with MLOps practices and tools (feature stores, data/version management like DVC/LakeFS, workflow orchestration like Airflow/Dagster).<br/><br/>- Experience with Natural Language Processing (NLP), computer vision, recommendation/ranking, or time-series forecasting.<br/><br/>- Familiarity with dashboarding/visualization for analysis and monitoring (Matplotlib, Plotly, Grafana, Streamlit).</p><br/></p> (ref:hirist.tech)