AI/ML Engineer

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About the Client:

Our client is a global technology research and advisory organization that partners with leading enterprises to drive data-driven decision-making and innovation.

Role: AI/ML Engineer

We are looking for several AI and ML Engineering team members who can operate across the full ML platform lifecycle, from building data pipelines and training workflows to deploying, scaling, and monitoring production ML systems. This role blends ML engineering, cloud, and DevOps, MLOps, and API and integration engineering. Depending on seniority, you may also provide technical leadership, set standards, and guide delivery across multiple workstreams.

General Information:

You will partner with product and engineering stakeholders to design, build, and operate reliable ML-enabled services in cloud environments. The work includes operational automation, event-driven and streaming integrations, and production-grade deployment and monitoring practices. The environment uses AWS heavily and may involve multi-cloud patterns and Azure DevOps for CI/CD.

Tasks and Deliverables:

• Design and implement end-to-end ML workflows including data ingestion, feature generation, training, evaluation, deployment, and monitoring
• Build and maintain data pipeline architectures and ETL or ELT workflows using AWS services such as S3, Glue, and Kinesis
• Deploy ML models to production using AWS services such as SageMaker and containerized runtimes on ECS, EKS, and Kubernetes
• Implement model monitoring, observability, alerting, and automated remediation for production ML systems
• Develop backend services and internal tooling in Python, including automation, integrations, and API development
• Build and maintain APIs using FastAPI or Flask to expose model inference and ML platform capabilities
• Implement event-driven architectures and streaming integrations, with Kafka as a primary technology where applicable
• Create and maintain CI/CD pipelines using Azure DevOps and AWS-native tooling to support rapid and reliable releases
• Apply infrastructure as code and configuration management practices to ensure reproducible environments and secure deployments
• Improve operational automation and reliability across ML platform components, including scaling, cost controls, and incident response
• For lead-level scope: set technical direction, define platform standards, review designs and code, mentor engineers, and drive cross-team execution

Required Experience:

• AI and ML Engineer scope: 5+ years of experience in cloud engineering, DevOps, or ML engineering with a focus on operational automation
• Lead AI and ML Engineer scope: 7+ years of experience in cloud engineering, DevOps, or ML engineering with a focus on operational automation, including ownership of technical direction for complex systems
• Hands-on experience with AWS services, including SageMaker, S3, Glue, Kinesis, ECS, and EKS
• Strong Kubernetes experience, including container orchestration in production and familiarity with multi-cloud environments
• Strong Python programming skills, including automation, integration, and API development
• Experience deploying and monitoring ML models in production environments, including observability and lifecycle management
• Knowledge of event-driven architectures and streaming technologies, especially Kafka
• Familiarity with CI/CD practices using Azure DevOps and AWS DevOps tooling
• Understanding of data pipeline architectures and experience building ETL or ELT workflows
• Experience building APIs with FastAPI or Flask
• Knowledge of infrastructure as code and configuration management tools

Engagement Highlights:

• Opportunity to build and operate production ML systems that prioritize reliability, automation, and scale
• Work across ML platform components including data pipelines, streaming, model serving, and observability
• Modern cloud and container stack with strong emphasis on Kubernetes and AWS services
• Clear runway for impact through operational automation, platform standardization, and measurable improvements to deployment velocity and system stability
• Lead-level scope available for candidates who can mentor, set direction, and drive execution across teams and stakeholders

Additional Details:

• Location: Remote in India
• Type: 40h/week Contract
• Timezone: Primarily IST (India Standard Time); Flexible schedule with required overlap for EST meetings; Estimated overlap window: 3-7 PM IST for client meetings
• Duration: 6–12 months with strong potential for extension
• Authorization: Applicants must be authorized to work in their country of residence without employer sponsorship