ML Engineer Remote from EU US business hours overlap

VIRTUSA REMOTE 2026-09-16

Strong experience as an ML Engineer / AI Engineer.
Hands-on experience with Generative AI and LLM-based solutions.
Practical experience with Amazon Bedrock and AWS.
Strong knowledge of RAG, prompt engineering, foundation models, and AI agents.
Experience integrating AI solutions with enterprise data sources and APIs.
Understanding of MLOps, model evaluation, observability, security, and AI guardrails.
Strong software-engineering and production deployment skills.
Ability to work effectively with cross-functional technical and product teams.
English at C1 level, with strong communication skills.
Ability to work remotely from the EU with significant US time-zone overlap.

At Virtusa, every innovator has the potential to transform and lead in a digital world—but unlocking that potential takes more than technology; it takes a trusted partner who combines engineering excellence, creativity, and an AI-first mindset.

Together, we co-create solutions that help businesses grow faster, operate smarter, and make experiences better with technology.

Job Description

This is a remote position.

We are looking for an experienced ML Engineer to design, develop, integrate, and operationalize Generative AI solutions using Amazon Bedrock across patient-access, engagement, and analytics platforms.

The role will focus on bringing AI capabilities into production, from foundation-model evaluation and prompt engineering through to RAG, AI agents, enterprise integrations, model evaluation, security, and observability.

This Role requires overlap with US Business hours

,[ Design and develop Generative AI solutions using Amazon Bedrock., Evaluate and integrate foundation models for different business use cases., Develop prompt-engineering and Retrieval-Augmented Generation (RAG) solutions., Build AI agents and model-enabled workflows., Integrate Bedrock with enterprise data sources, APIs, and existing applications., Implement model evaluation, guardrails, security, observability, and cost optimization., Deploy and operationalize reliable, scalable, and secure AI solutions in production., Apply strong software engineering, AWS, API integration, and MLOps practices., Collaborate with Data Engineers, Software Engineers, Product teams, and SRE/RunOps teams.] Requirements: AI, Analytics platform, Security, Use cases, AWS, API, MLOps, SRE, Communication skills Additionally: Remote work.