ML Engineer

Polestar Analytics; Kolkata District; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Polestar Analytics is hiring an ML Engineer – AI/ML Platform & MLOps with 3–10 years of experience to design, deploy, and monitor enterprise AI solutions. You will architect scalable data pipelines, feature stores, and model serving for real-time and batch workloads.

You will lead MLOps practices, CI/CD, model versioning, and automated retraining across cloud and on‑prem environments while collaborating with data scientists, engineers, and business stakeholders to deliver impactful AI products.

Qualifications

• 3–10 years of experience in ML engineering, AI platform engineering, or MLOps.
• Hands-on experience with end-to-end ML lifecycle including data engineering and deployment.
• Experience building scalable AI applications, inference services, and ML APIs.

Responsibilities

• Design 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 ML models for business use cases (forecasting, churn, recommendations, NLP, time series).
• Create AI-powered apps, decision-support systems, and production-grade ML services.
• Develop APIs, microservices, inference services, and scoring engines for real-time and batch serving.
• Implement robust MLOps pipelines with CI/CD, experiment tracking, and model versioning.
• Automate model retraining and CD pipelines across cloud and on-prem environments.
• Monitor model/data drift, explainability, fairness, and performance degradation.
• Contribute to enterprise AI platforms with reusable components and governance.

Skills

ML lifecycle

Python

CI/CD

Data engineering

Model deployment

APIs

Education

Bachelor's degree in CS/AI/DS

Tools

PySpark

Databricks

Airflow

REST APIs

Job description

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:

• Data Engineering: SQL, PySpark, Databricks, Apache Spark, Airflow, BigQuery
• Programming: Python, FastAPI, Flask, REST APIs

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.