Python Engineer - Artificial Intelligence/Machine Learning

We are looking for an experienced AIML Python Engineer with strong expertise in building, deploying, and maintaining end-to-end ML pipelines and APIs. The ideal candidate will have deep hands-on experience with Python, AWS SageMaker, CI/CD for ML workflows, and scalable data processing frameworks. You will be responsible for enabling real-time, batch, event-triggered, and edge ML deployments while collaborating with cross-functional teams to deliver high-quality solutions.

Key Responsibilities :

- Design, develop, and maintain ML workflows and pipelines using Python.

- Deploy ML models in real-time, batch, event-driven, and edge environments.

- Implement and manage ML pipelines using AWS SageMaker (Pipelines, MLflow, Feature Store).

- Build and deploy APIs for ML workflows using FastAPI, Flask, or Django.

- Ensure APIs are secure, scalable, and optimized for performance.

- Work on end-to-end ML lifecycle: model development, training, validation, deployment, and monitoring.

- Apply ML frameworks & libraries such as Scikit-learn, PyTorch, XGBoost, LightGBM, MLflow.

- Implement CI/CD pipelines for ML workflows using Bitbucket, Jenkins, Nexus, and other tools.

- Use Autosys (or similar) for job scheduling and workflow automation.

- Develop ETL pipelines using PySpark, Kafka, AWS EMR Serverless.

- Handle large-scale data ingestion, transformation, and feature engineering for ML systems.

- Collaborate with data scientists, data engineers, and DevOps teams.

- Advocate for MLOps best practices including versioning, reproducibility, monitoring, and scalability.

- Contribute to process improvement and innovation in ML system design and deployment.

Required Skills & Qualifications :

- 5+ years of experience in Python for ML workflows and pipeline development.

- 4+ years of hands-on experience with AWS SageMaker for ML deployment (Pipelines, MLflow, Feature Store).

- 3+ years in API development with FastAPI, Flask, Django.

- Strong experience in ML frameworks : Scikit-learn, PyTorch, XGBoost, LightGBM, MLflow.

- Solid understanding of the ML lifecycle : development, training, validation, deployment, and monitoring.

- Strong knowledge of CI/CD pipelines for ML workflows (Bitbucket, Jenkins, Nexus, Autosys).

- Hands-on experience with ETL pipelines using PySpark, Kafka, and AWS EMR Serverless.

- Experience with H2O.ai framework.

- Exposure to real-time ML at scale in production-grade systems.

- Experience in edge ML deployments.

- Strong analytical and problem-solving abilities.

- Ability to work independently as well as in collaborative team environments.

- Strong communication skills to articulate technical solutions to cross-functional teams.

- Opportunity to work on cutting-edge ML deployment use cases.

- Exposure to large-scale, production-grade ML systems in real-world environments.

- Contract-to-hire opportunity with full-time absorption by client.

- Work in a collaborative, innovation-driven environment.