<p>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.</p><p><br/></p><p> </p><p>Key Responsibilities :</p><p><br/></p><p> </p><p> </p><p>- Design, develop, and maintain ML workflows and pipelines using Python.</p><p> </p><p><br/></p><p>- Deploy ML models in real-time, batch, event-driven, and edge environments.</p><p> </p><p><br/></p><p>- Implement and manage ML pipelines using AWS SageMaker (Pipelines, MLflow, Feature Store).</p><p> </p><p><br/></p><p>- Build and deploy APIs for ML workflows using FastAPI, Flask, or Django.</p><p> </p><p><br/></p><p>- Ensure APIs are secure, scalable, and optimized for performance.</p><p> </p><p><br/></p><p>- Work on end-to-end ML lifecycle: model development, training, validation, deployment, and monitoring.</p><p> </p><p><br/></p><p>- Apply ML frameworks & libraries such as Scikit-learn, PyTorch, XGBoost, LightGBM, MLflow.</p><p> </p><p><br/></p><p>- Implement CI/CD pipelines for ML workflows using Bitbucket, Jenkins, Nexus, and other tools.</p><p> </p><p><br/></p><p>- Use Autosys (or similar) for job scheduling and workflow automation.</p><p> </p><p><br/></p><p>- Develop ETL pipelines using PySpark, Kafka, AWS EMR Serverless.</p><p> </p><p><br/></p><p>- Handle large-scale data ingestion, transformation, and feature engineering for ML systems.</p><p> </p><p><br/></p><p>- Collaborate with data scientists, data engineers, and DevOps teams.</p><p> </p><p><br/></p><p>- Advocate for MLOps best practices including versioning, reproducibility, monitoring, and scalability.</p><p> </p><p><br/></p><p>- Contribute to process improvement and innovation in ML system design and deployment.</p><p> </p><p><br/></p><p>Required Skills & Qualifications :</p><p><br/></p><p> </p><p>- 5+ years of experience in Python for ML workflows and pipeline development.</p><p> </p><p><br/></p><p>- 4+ years of hands-on experience with AWS SageMaker for ML deployment (Pipelines, MLflow, Feature Store).</p><p> </p><p><br/></p><p>- 3+ years in API development with FastAPI, Flask, Django.</p><p> </p><p><br/></p><p>- Strong experience in ML frameworks : Scikit-learn, PyTorch, XGBoost, LightGBM, MLflow.</p><p> </p><p><br/></p><p>- Solid understanding of the ML lifecycle : development, training, validation, deployment, and monitoring.</p><p> </p><p><br/></p><p>- Strong knowledge of CI/CD pipelines for ML workflows (Bitbucket, Jenkins, Nexus, Autosys).</p><p> </p><p><br/></p><p>- Hands-on experience with ETL pipelines using PySpark, Kafka, and AWS EMR Serverless.</p><p> </p><p><br/></p><p>- Experience with H2O.ai framework.</p><p> </p><p><br/></p><p>- Exposure to real-time ML at scale in production-grade systems.</p><p> </p><p><br/></p><p>- Experience in edge ML deployments.</p><p> </p><p><br/></p><p>- Strong analytical and problem-solving abilities.</p><p> </p><p><br/></p><p>- Ability to work independently as well as in collaborative team environments.</p><p> </p><p><br/></p><p>- Strong communication skills to articulate technical solutions to cross-functional teams.</p><p> </p><p><br/></p><p>- Opportunity to work on cutting-edge ML deployment use cases.</p><p> </p><p><br/></p><p>- Exposure to large-scale, production-grade ML systems in real-world environments.</p><p> </p><p><br/></p><p>- Contract-to-hire opportunity with full-time absorption by client.</p><p> </p><p><br/></p><p>- Work in a collaborative, innovation-driven environment.</p> (ref:hirist.tech)