Virtusa is seeking AI Engineers who design, build, and operationalize next-generation AI applications that deliver measurable business impact. The role involves transforming business requirements into production-grade AI systems and collaborating with various experts to implement cutting-edge AI solutions.

Responsibilities

• 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
• 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
• 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
• 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

Skills

• 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
• 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
• Must be legally authorized to work in the United States
• Master's degree in Computer Science, Engineering, Data Science, or a related quantitative field (or equivalent practical experience) is strongly preferred
• Familiarity with regulated domains (finance, healthcare, telecom) and AI compliance frameworks is a plus

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