Description :

Position : AI/ML Engineer

Experience : 5+ Years (Cloud Engineering / DevOps / ML Engineering)

Notice Period : Immediate to 15 Days

Mandatory Skills : (must be reflected in project descriptions, not just skills section)

Tech Stack Table :

Skill Area :

Required Experience :

- Cloud Engineering / DevOps / ML Engineering: 5+ years

- AWS Services (SageMaker, S3, Glue, Kinesis, ECS, EKS): Hands-on

- Kubernetes (Container Orchestration): Strong expertise

- Multi-cloud Environments: Practical exposure

- Python Programming: Advanced (automation, integration, API development)

- ML Model Deployment & Monitoring: Production-level experience

- Event-driven Architectures & Streaming (Kafka): Strong knowledge

- CI/CD (Azure DevOps, AWS DevOps): Hands-on

- Data Pipeline Architectures (ETL/ELT workflows): Proven track record

- API Development (FastAPI, Flask): Hands-on

- Infrastructure as Code / Config Management Tools: Working knowledge

Responsibilities :

- Design and implement scalable ML engineering solutions with automation focus.

- Deploy and monitor ML models in production environments.

- Build and optimize data pipelines (ETL/ELT workflows).

- Develop APIs using FastAPI/Flask for ML integration.

- Work with AWS services (SageMaker, Glue, Kinesis, ECS/EKS, S3).

- Manage container orchestration with Kubernetes in multi-cloud setups.

- Implement CI/CD pipelines using Azure DevOps and AWS DevOps.

- Collaborate with IT and business stakeholders to ensure successful delivery.

- Apply Infrastructure as Code and configuration management best practices.