Ai Ml Engineer

Job Description

MLOps Infrastructure & Automation: • Design and implement scalable and reliable MLOps pipelines on AWS. • Automate ML inferencing deployment, monitoring, and maintenance workflows. • Build and maintain infrastructure as code (IaC) using tools like Terraform. • Implement CI/CD pipelines for API/ ML model deployment and infrastructure changes. • Establish and maintain robust monitoring and logging solutions for ML models and infrastructure using AWS services. • Manage the model registry and deployment process. Deployment and Scaling: • Deploy pre-trained machine learning models on AWS using services like SageMaker and EKS • Optimize model deployment for performance and scalability. • Implement strategies for model versioning and rollback. • Manage the scaling of deployed models based on demand. • Develop Microservices leveraging SOLR and ML Endpoints for Integration Data Engineering & Management: • Design and implement data pipelines for model inference using AWS services like S3, Glue, and Athena. • Ensure data quality and consistency for model inference. • Optimize data storage and retrieval for inference performance. Collaboration & Communication: • Collaborate with data scientists and fellow engineers to ensure smooth model deployment and operation. • Communicate technical concepts and findings effectively to both technical and non-technical audiences. • Participate in code reviews and contribute to best practices established in the team. • Troubleshoot and resolve production issues related to ML models and infrastructure. What you will need • Bachelor's or Master's degree in Computer Science, Engineering, or a related field with 3-9 years of experience working data and ML services using Python. • Strong proficiency in Python and API development using Python frameworks like Fast API. • Experience with AWS cloud services particularly Sagemaker, Lambda, EKS, S3, Glue, and Terraform or other equivalent cloud alternatives. • Exposure with CI/CD pipelines and DevOps practices. • Experience with containerization technologies (e.g., Docker, Kubernetes). • Strong problem-solving and analytical skills. • Ability to work within a team environment. • Exposure with MLOps platforms and tools. • Exposure deploying real-time ML inference. • Exposure with Big data technologies and data engineering • Experience working in an Agile environment