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.