ML Engineer

Job Title:ML Engineer – AI/ML Platform & MLOps

Location: Noida | Bangalore | Kolkata

Employment Type: Full-time

Experience: 3–10 Years

Industry Focus: IT Services, Artificial Intelligence & Analytics

Position Summary

We are seeking a skilled ML Engineer – AI/ML Platform & MLOps with 3–10 years of experience in building, deploying, monitoring, and scaling end-to-end Machine Learning solutions. The ideal candidate will have expertise across the complete AI/ML lifecycle, including data engineering, feature engineering, model development, deployment, MLOps, monitoring, governance, and AI application development. This role involves designing scalable AI platforms, productionizing ML models, and enabling enterprise-wide AI adoption through robust engineering practices.

Strategic Responsibilities

• Design and develop scalable data pipelines for structured and unstructured data to support enterprise AI initiatives.
• Build reusable feature engineering frameworks, feature stores, and data quality validation pipelines.
• Develop, train, optimize, and deploy Machine Learning models for business use cases such as demand forecasting, demand sensing, customer churn prediction, recommendation systems, price elasticity, optimization, NLP, regression, classification, and time-series forecasting.
• Build AI-powered business applications, intelligent decision-support systems, and production-grade ML services.
• Develop APIs, microservices, inference services, and scoring engines for real-time and batch model serving.
• Design and implement robust MLOps pipelines, including CI/CD workflows, automated model deployment, experiment tracking, and model versioning.
• Build automated model retraining and continuous delivery pipelines across cloud and on-premise environments.
• Implement monitoring frameworks for model drift, data drift, concept drift, explainability, fairness, bias detection, and performance degradation.
• Contribute to the development of enterprise AI/ML platforms, reusable ML components, accelerators, and governance frameworks.
• Develop monitoring dashboards, operational metrics, and governance workflows to ensure reliable AI system performance.
• Collaborate with Data Scientists, Data Engineers, Product teams, and Business stakeholders to build scalable AI solutions.
• Continuously evaluate emerging AI/ML technologies and integrate engineering best practices into platform development.

Required Experience:

• 3–10 years of experience in Machine Learning Engineering, AI Platform Engineering, or MLOps.
• Strong experience developing and deploying production-grade Machine Learning solutions.
• Hands-on experience with end-to-end ML lifecycle, including data engineering, feature engineering, model training, deployment, and monitoring.
• Experience building scalable AI applications, inference services, and ML APIs.
• Strong understanding of MLOps practices including CI/CD, model versioning, experiment tracking, and automated retraining.
• Experience deploying Machine Learning solutions on cloud platforms and production environments.
• Knowledge of model monitoring, governance, explainability, fairness, and responsible AI practices.
• Strong understanding of scalable software engineering principles and distributed ML systems.

Technical Skills:

• Machine Learning: Scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow, PyTorch
• Data Engineering: SQL, PySpark, Databricks, Apache Spark, Airflow, BigQuery
• MLOps: MLflow, Kubeflow, SageMaker, Vertex AI, Azure Machine Learning, Databricks
• Programming: Python, FastAPI, Flask, REST APIs
• Cloud Platforms: Microsoft Azure, AWS, Google Cloud Platform (GCP)
• Containers & DevOps: Docker, Kubernetes, Terraform, GitHub Actions, Jenkins

Good to Have:

• Experience developing enterprise-scale AI products and intelligent business applications.
• Hands-on experience working with end-to-end AI/ML platforms.
• Exposure to LLMOps, Generative AI deployment, and modern AI platform architectures.
• Understanding of feature stores, model registries, and metadata management.
• Experience deploying highly scalable, distributed Machine Learning systems in production.
• Familiarity with AI governance, model observability, and cloud-native ML infrastructure.

Educational Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related field.

Soft Skills:

• Strong analytical and problem-solving skills.
• Excellent communication and collaboration abilities.
• Ability to work effectively in cross-functional and agile teams.
• Strong ownership mindset with a focus on delivering scalable AI solutions.
• Passion for innovation and continuous learning in emerging AI technologies.
• Ability to manage multiple priorities in a fast-paced environment.
• Detail-oriented with a strong focus on quality, performance, and business impact.