Polestar Analytics - Machine Learning Engineer

Polestar Analytics; Kolkata District; On-site; INR 1,500,000 - 2,300,000; Full time

Job summary

Polestar Analytics is seeking an experienced ML Engineer to lead AI/ML platform and MLOps work. You will design scalable data pipelines, build feature stores, and deploy ML models for various business use cases.

The role requires strong Python skills, expertise in ML tooling, and collaboration with data scientists and engineers. You will contribute to enterprise AI platforms, monitor model performance, and implement robust MLOps pipelines across cloud and on‑prem environments.

Qualifications

• Experience building end-to-end ML solutions across lifecycle.
• Strong knowledge of ML algorithms and model deployment.
• Experience with MLOps and monitoring for drift/fairness.
• Proficiency in Python and building REST APIs.
• Experience on cloud platforms and data engineering basics.

Responsibilities

• Design scalable data pipelines for structured/unstructured data.
• Develop feature stores and data quality validation pipelines.
• Build, train, optimize, and deploy ML models for business use cases.
• Create AI-powered apps and real-time inference services.
• Develop CI/CD for ML including experiment tracking and versioning.
• Automate model retraining and cross-environment delivery.
• Implement monitoring for drift, explainability, bias, and performance.
• Collaborate with data scientists, engineers, and stakeholders.

Skills

Scikit-learn

XGBoost

LightGBM

CatBoost

TensorFlow

PyTorch

SQL

PySpark

Databricks

Apache Spark

Airflow

BigQuery

MLflow

Kubeflow

SageMaker

Vertex AI

Azure ML

Python

FastAPI

Flask

REST APIs

Azure

AWS

GCP

Docker

Kubernetes

Terraform

GitHub Actions

Jenkins

Education

Bachelor's or Master's in CS/AI/ML/DS

Tools

MLflow

Kubeflow

SageMaker

Vertex AI

Azure ML

Databricks

Job description

Position Summary

We are seeking a skilled ML Engineer - AI/ML Platform & MLOps with 3 - 8 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.

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.

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.

Educational Qualifications

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

(ref:hirist.tech)