Machine Learning / GenAI Engineer

Location - Noida, Bengaluru, Chennai, Hyderabad & Pune.

ML Engineer

The Role

The Machine Learning Engineer is responsible for developing, training, deploying, and maintaining machine learning models that solve business problems using structured and unstructured data. The role bridges data science and production engineering.

Responsibilities:

• Design, develop, and deploy end‑to‑end machine learning solutions, covering data ingestion, preprocessing, model training, inference, and system integration.
• Build, train, fine‑tune, and evaluate machine learning and deep learning models for use cases such as prediction, classification, clustering, recommendation, anomaly detection, and forecasting.
• Perform feature engineering, data transformation, and exploratory data analysis to improve model performance and reliability.
• Develop and maintain robust data pipelines for structured and unstructured data, including data validation, cleansing, anonymization, and feature extraction.
• Implement and integrate ML models using modern machine learning frameworks and libraries to support enterprise and client‑driven use cases.
• Build scalable and containerized ML applications using Docker and deploy them across development, testing, and production environments.
• Develop automation scripts, APIs, and backend services using Python and related tools to support model training, inference, and monitoring workflows.
• Design and implement end‑to‑end ML pipelines, including model training, validation, experiment tracking, versioning, deployment, and retraining.
• Work with computer vision, NLP, or time‑series models where required to support multimodal or domain‑specific AI solutions.
• Collaborate with cross‑functional teams, including data engineers, cloud architects, MLOps engineers, and business stakeholders, to translate business requirements into ML‑driven solutions.
• Ensure proper code versioning, reproducibility, and collaboration through source control systems and ML development best practices.
• Monitor and optimize model and application performance with respect to accuracy, latency, scalability, and resource utilization.
• Document models, data pipelines, experiments, and system behavior to enable knowledge sharing, auditability, and long‑term maintenance. Define and monitor service‑level indicators and objectives (SLIs/SLOs) for latency, throughput, error rates, and model accuracy, supported by centralized observability dashboards and alerts.
• Collaborate with Data Engineering, Platform/Infrastructure, and Product teams to standardize reusable libraries, Helm‑based deployment patterns, cost‑efficient scaling strategies, and operational runbooks.
• Maintain model cards, data sheets, evaluation reports, and operational SOPs
• Create and maintain internal libraries: data transforms, evaluation harnesses, serving wrappers.
• Provide Helm charts/manifests for standard model serving patterns.
• Enforce experiment tracking, registry usage, and promotion gates with automated evaluations.
• Define data governance: versioning, PII redaction, audit trails, lineage requirements.
• Maintain framework benchmarks (training throughput, inference latency) across hardware profiles.

Qualifications & Experience

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, or related discipline
• 3–5 years of experience in machine learning, applied data science, deploying and maintaining ML models in production environments
• Strong ML engineering foundation, with proficiency in Python and hands‑on experience using scikit‑learn and deep learning framework (TensorFlow, PyTorch) for model development, training, evaluation, and optimization.
• Expertise in data preprocessing and feature engineering, designing robust pipelines for batch and real‑time inference while ensuring consistency and parity between training and production environments.
• Proven MLOps capability, including experiment tracking, model registry and versioning, CI/CD automation, production monitoring, and data/model drift detection to support reliable ML lifecycles.
• Experience in production deployment of ML models, delivering scalable REST/gRPC inference services using containerization and Kubernetes‑based orchestration, with attention to performance, reliability, and observability.
• Strong governance, scalability, and optimization focus, encompassing reproducibility and lineage, GPU‑aware training and inference, cost‑performance optimization, large‑scale experimentation (A/B, canary, shadow), and adherence to security and compliance standards.

Certifications Required:

• Certified Associate in Python Programming
• Certified Associate in Python for Data Science
• Docker Certified Associate (DCA)
• Kubernetes Application Developer