ML Engineer(AWS)

🚀 Hiring | ML Engineer – AWS

Are you an AI/ML Engineer who knows how to take machine learning models beyond experimentation and run them reliably in production?

We are looking for a hands-on ML Engineer with strong AWS and MLOps expertise to build, deploy, secure and operate production-grade machine learning systems.

Key Requirements:

🔹 AWS & ML Engineering

• AWS SageMaker, Bedrock, EC2, ECS/EKS.

• S3, Lambda, Step Functions, IAM, VPC and CloudWatch.

• End-to-end ML pipelines for training, evaluation and deployment.

🔹 MLOps & DevOps

• CI/CD pipelines for ML workloads.

• Terraform / CloudFormation.

• Model packaging, versioning and promotion across environments.

• MLflow, SageMaker Pipelines and Model Registry.

🔹 Model Monitoring & Optimisation

• Model monitoring and drift detection.

• CloudWatch and SageMaker Model Monitor.

• Training and inference optimisation.

• Cost optimisation, autoscaling and production reliability.

🔹 Development & Security

• Strong Python programming.

• Docker and Kubernetes (EKS/ECS).

• AWS IAM, KMS, encryption and network security.

• Secure MLOps practices and production monitoring.

Experience: 4+ years in AI/ML and cloud platform engineering, with significant hands-on AWS experience.

Good to Have:

• Generative AI / AWS Bedrock experience.

• Feature Store and model registry.

• ML security, AI guardrails and prompt injection mitigation.

• AWS ML, DevOps or Security certifications.

🎯 Who should apply?

ML Engineers, MLOps Engineers and AI/ML Platform Engineers who have built and operated production ML systems on AWS — not just trained models or developed prototypes.

📩 Interested candidates can share their updated profiles at

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