Virtusa AI Labs is seeking AI Engineers who design, build, and operationalize next-generation AI applications that deliver measurable business impact. This role blends technical engineering, applied machine learning, and Agentic System development to help enterprise clients move from AI experimentation to large-scale adoption.

As part of a high-impact AI 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 and Industry trends 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 Responsibilities

Agentic Solution Design & Development

• Collaborate with Engineering teams to design and implement end-to-end AI solutions using RAG, Agentic Patterns, LLM fine-tuning, multi-agent workflows, and custom model integration patterns.

• Develop and customize LLMs using PEFT methods (LoRA, QLoRA, adapters) and work with Mixture-of-Experts (MoE) based architectures where applicable.

Full-Cycle AI Engineering

• Be part of the full AI lifecycle — from data ingestion and preparation through model training,selection,evaluation, production deployment and monitoring.

• Apply MLOps, LLMOps, and FMOps principles to automate workflows, manage versions, and ensure robust, repeatable deployments.

• Develop appropriate techniques for data drift, model drift and intent drift

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 IT and governance standards and 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.

What We Look For

Technical Depth

• Preferred experience of 3 years (minimum of 1 year) in AI/ML engineering, Generative AI, Agentic AI, data engineering, or software development.

• Proficiency in Python and libraries such as PyTorch, TensorFlow, Scikit-learn, Transformers, LangChain, LlamaIndex, AWS Bedrock , GCP ADK , Azure MAF or DSPy.

• Strong knowledge of data pipelines, vector databases, embedding, chunking techniques and orchestration tools (e.g., Airflow, Spark, Ray).

• Experience/Knowledge with LLM fine-tuning using PEFT (LoRA, QLoRA, adapters) and familiarity with MoE-based model configurations.

• Experience/Awareness of deploying AI solutions in cloud or hybrid environments using containers, APIs, and CI/CD.

Problem-Solving & Collaboration

• Ability to translate technical innovation into real-world business solutions.

• Comfort working across functional boundaries—data science, engineering, product, and internal practitioners.

• Strong written and verbal communication skills, with the ability to simplify complex concepts for diverse audiences.

Education & Background

• Master’s degree in Computer Science, Engineering, Data Science, or a related quantitative field (or equivalent practical experience) is strongly preferred. Bachelor’s degree may be considered if the candidate has substantially the same knowledge level and performed substantially the same work as employees with advanced degrees.

• Familiarity with regulated domains (finance, healthcare, telecom) and AI compliance frameworks is a plus.

• Must be legally authorized to work in the United States

Equal Opportunity Statement

This organization is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, gender identity, sexual orientation, or any other characteristic protected by applicable federal, state, or local law.