ML Engineer – AI/ML Platform & MLOps

Company Description

CodeChavo is a trusted IT staffing and services provider, supporting top brands across India and the US with tailored staffing and innovative solutions. Renowned for its expertise, the company focuses on delivering value and helping businesses achieve their technology goals. With a commitment to excellence, CodeChavo fosters collaboration to drive business success and build impactful partnerships.

Experience : 3-10 years

Location : Noida , Banglore , Hyderabad , Kolkata

Role Description

We are looking for an experienced ML Engineer to build, deploy, monitor, and scale end-to-end machine learning solutions across predictive analytics, optimization, forecasting, recommendation systems, and AI-driven applications. The role spans the complete AI/ML lifecycle, including data engineering, feature engineering, model development, deployment, MLOps, monitoring, governance, and AI application development.

Key Responsibilities

Data & Feature Engineering - Build scalable data pipelines for structured and unstructured data, Develop feature engineering frameworks and reusable feature stores, Implement data quality and validation frameworks.

Machine Learning Model Development - Develop and operationalize models across areas such as - Demand Forecasting, Demand Sensing, Customer Churn Prediction, Price Elasticity Modeling, Recommendation Systems, Classification Models, Regression Models, Time Series Forecasting, Optimization Models, NLP Models

AI Application Development - Develop AI-powered business applications and decision-support systems, Create APIs and microservices for model serving, Build user-facing applications using AI frameworks, Develop model inference services and scoring engines.

MLOps & Platform Engineering - Design and implement CI/CD pipelines for ML, Develop model versioning and experiment tracking frameworks, Build automated retraining pipelines, Create model deployment workflows across cloud and on-prem environments.

Model Monitoring & Governance - Implement monitoring for - Model Drift, Data Drift, Concept Drift, Performance Degradation, Explainability, Fairness, Bias Detection

Platform Development - Contribute to enterprise AI/ML platforms, Build reusable ML services and accelerators, Develop monitoring dashboards and governance workflows.

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 ML, Databricks

Programming - Python, FastAPI,Flask, REST APIs

Cloud Platforms - Azure, AWS, GCP

Containers & DevOps - Docker, Kubernetes, Terraform, GitHub Actions, Jenkins

Preferred Qualifications

Experience developing enterprise AI products.

Experience working with end-to-end AI/ML platforms.

Exposure to MLOPS and LLMOps.

Understanding of feature stores and model registries.

Experience in deploying scalable ML systems in production.