Virtusa - AI Technical Architect

Role Overview : We are looking for an experienced AI Technical Architect to lead the architecture, development, and production deployment of enterprise Generative AI, RAG, and Agentic AI solutions. The role requires a strong combination of hands-on engineering, AI architecture, technical leadership, and client engagement. Key Responsibilities : - Lead the design and architecture of end-to-end AI and GenAI solutions, with a strong focus on RAG architectures and Agentic AI for complex business workflows. - Define scalable, secure, low-latency, and cost-efficient architectures for enterprise AI applications. - Drive the transition of GenAI solutions from PoC to production, including architecture, performance optimization, scalability, deployment, and operational readiness. - Design and implement RAG pipelines, including document ingestion, chunking, embeddings, retrieval, reranking, context management, and response generation. - Architect and develop Agentic AI solutions, applying agentic patterns, context engineering, tool/function calling, memory management, and multi-agent workflows. - Provide hands-on technical leadership by writing code, conducting code reviews, troubleshooting complex issues, and unblocking engineering teams. - Define and implement AI evaluation frameworks using approaches and tools such as RAGAS and G-Eval to measure accuracy, faithfulness, relevance, hallucination, and overall model performance. - Establish AI safety and governance mechanisms covering guardrails, responsible AI, data privacy, security, bias mitigation, cost tracking, rate limiting, and compliance. - Work closely with clients and business stakeholders to translate ambiguous business problems into AI strategies, technical roadmaps, and scalable solutions. - Act as the primary technical point of contact for AI initiatives and communicate complex AI concepts effectively to both technical and non-technical stakeholders. - Mentor and guide AI/ML engineers and developers on architecture, coding practices, solution design, and emerging GenAI technologies. - Evaluate emerging AI models, frameworks, tools, and architectural patterns and recommend appropriate technologies based on business requirements. - Collaborate with engineering, data, cloud, security, and product teams to ensure successful delivery of AI solutions. Requirements : - 13 - 20 years of experience in software engineering, AI/ML, data science, or related technology roles, including 3+ years of hands-on experience in Generative AI. - Proven experience architecting and deploying GenAI applications in production, preferably with at least two high-traffic production implementations. - Expert-level understanding of LLMs such as GPT, Claude, Llama, and Gemini, along with prompt engineering and model customization/fine-tuning techniques. - Strong hands-on experience with RAG architectures, embeddings, vector search, retrieval strategies, and context engineering. - Strong knowledge of Agentic AI patterns, agent orchestration, tool/function calling, memory management, and multi-agent workflows. - Hands-on experience with frameworks such as LangChain, LlamaIndex, CrewAI, or equivalent orchestration frameworks. - Strong foundation in traditional AI/ML, including supervised and unsupervised learning, NLP, computer vision, and statistical modeling. - Strong experience with vector databases such as Pinecone, Weaviate, Milvus, or equivalent technologies. - Experience designing and implementing MLOps/LLMOps pipelines for model deployment, monitoring, evaluation, versioning, and lifecycle management. - Strong understanding of AI governance, security, privacy, responsible AI, guardrails, cost optimization, and regulatory considerations. - Strong programming and software engineering skills with the ability to contribute hands-on to production code. - Proven experience providing technical mentorship, conducting code reviews, and leading engineering teams through complex technical challenges. - Excellent client-facing, communication, presentation, and stakeholder management skills, with the ability to engage with C-suite and senior business stakeholders. - Bachelor's or Master's degree in Computer Science, Mathematics, Engineering, or a related discipline is preferred.