AI Solution LeadRole OverviewVirtusa AI Labs is seeking AI Solution Development Engineers who design, build, and operationalize next-generation AI applications that deliver measurable business impact.
This role blends deep technical engineering, applied machine learning, and solution development to help enterprise clients move from AI experimentation to large-scale adoption.
As part of a high-impact, client-embedded engineering team, you will transform business requirements into production-grade AI systems, spanning GenAI applications, modern LLM fine-tuning, predictive modeling, and agentic AI systems. You'll work alongside Virtusa's data, product, and cloud experts to bring cutting-edge AI research into the enterprise world responsibly and at scale. If you thrive at the intersection of AI innovation, full-stack development, and business value creation, this is your opportunity to help shape the future of applied AI.
Key ResponsibilitiesSolutionDesign & DevelopmentFull-Cycle AI Engineering- Collaborate with client and internal teams to design and implement end-to-end AIsolutions using RAG, LLM fine-tuning, multi-agent workflows, and modern model integration patterns.
- Develop and customize LLMs using PEFT methods (LoRA, QLoRA, adapters) and work with Mixture-of-Experts (MoE) based architectures where applicable.
- Own the full AI lifecycle - from data ingestion and preparation through model training, evaluation, and production deployment.
- Apply MLOps, LLMOps, and FMOps principles to automate workflows, manage versions, and ensure robust, repeatable deployments.
System Integration & Deployment- Engineer data pipelines, APIs, and orchestration layers to integrate AI solutions with enterprise systems.
- Ensure solutions are secure, scalable, and compliant with client IT and governance policies.
Evaluation & Continuous Improvement- Build automated evaluation loops for model quality, bias detection, and performance monitoring.
- Quantify ROI and business value post-deployment and feed learnings back into reusable frameworks and blueprints.
Responsible AI & Governance- Embed transparency, explainability, audit trails, and data-lineage tracking in every solution.
- Partner with client risk and compliance teams to ensure adherence to Responsible AI principles.
