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AI/ML Engineer - Marga Darshana Consulting
ML Engineer
AI/ML Engineer with 5‑8 years experience building, deploying and scaling machine‑learning models on AWS, leveraging Python, Kubernetes, Docker, Kafka and DevOps tools to create automated, production‑grade pipelines.
About the role
Key Responsibilities
- Design, develop, and maintain end‑to‑end ML pipelines on AWS services such as SageMaker, S3, Glue, and Kinesis.
- Containerize model serving APIs using Docker and orchestrate them with Kubernetes (EKS/ECS) for high availability.
- Implement CI/CD workflows with Azure DevOps or AWS DevOps and infrastructure as code using Terraform or CloudFormation.
- Build real‑time data ingestion and streaming solutions with Kafka, ensuring reliable data flow for model training and inference.
- Develop and expose model inference services using FastAPI or Flask, incorporating monitoring, logging, and automated scaling.
Requirements
- 5–8 years of professional experience in AI/ML engineering and cloud platforms, primarily AWS.
- Proficiency in Python for data processing, model development, and API creation.
- Hands‑on experience with containerization (Docker) and orchestration (Kubernetes/EKS/ECS).
- Solid understanding of DevOps practices, CI/CD pipelines, and infrastructure‑as‑code tools (Terraform, CloudFormation).
- Experience building scalable, automated data pipelines and deploying ML models in production environments.
Skills
awspythonkubernetesdockerkafkafastapiterraform