Platform Engineer AI

Role Overview Virtusa is looking for a talented Platform Engineer with a strong AI and machine learning background to join its growing technology team in Chennai. In this role you will design, build, and integrate intelligent applications powered by large language models and generative AI, ensuring all solutions meet rigorous production standards for quality, reliability, and performance. Key Responsibilities - Design and develop production-grade AI and machine learning applications with a focus on LLMs and generative AI - Build scalable services and automation workflows using Python and modern software engineering best practices - Operationalise LLM-based solutions with robust reliability, performance monitoring, and governance controls - Implement advanced LLM application patterns including prompt orchestration, retrieval-augmented generation, embeddings, and vector databases - Collaborate with cross-functional teams to integrate AI capabilities into existing platforms and products Required Qualifications - Hands-on experience in Python development and production software engineering - Strong understanding of machine learning concepts and large language model architectures - Practical knowledge of LLM frameworks, RAG pipelines, vector databases, and embedding techniques - Experience deploying and operationalising ML models in production environments - Familiarity with cloud platforms and DevOps or MLOps practices Why Join Us Virtusa is a global technology services company known for its engineering-first culture and commitment to innovation at scale. This is an excellent opportunity to work on cutting-edge generative AI solutions and grow your career alongside some of the brightest minds in the industry.

- Python programming and production-level software development - Machine learning and large language model expertise - Experience with RAG, prompt orchestration, and vector databases - Knowledge of embeddings and LLM application patterns - MLOps and cloud deployment experience - Strong understanding of software engineering best practices