Job Description

Join Wipro's AI-Native Business & Platforms unit, a forward-thinking team dedicated to advancing the future of enterprise artificial intelligence. Our Agentic Hive Platform team is responsible for creating a robust, scalable platform that enables intelligent AI agents, digital ecosystems, and business process automation for our clients worldwide. This team plays a vital role in pioneering agentic AI technology, integrating delivery and client partnerships across India and the United States to drive innovation at enterprise scale.

We are looking for an experienced and hands-on Engineering Head to spearhead the technical vision and development of the Agentic Hive platform. The ideal candidate will lead architecture design, development, and deployment of agentic AI capabilities with a strong focus on security, scalability, and reliability. In this pivotal leadership role, you will guide a dynamic engineering team responsible for delivering production-ready AI solutions and integrating them seamlessly with enterprise systems.

Operating from India, you will collaborate closely with leadership and technical peers based in the U.S., ensuring alignment and fostering innovation across global teams. This is a unique opportunity to influence the trajectory of agentic AI in a large enterprise context, blending deep technical expertise with strategic leadership and enterprise architecture sensibilities.

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• Take full ownership of the architecture and engineering delivery of the Agentic Hive platform, ensuring it meets business priorities, is modular, scalable, and aligned with product roadmaps.
• Lead the design and hands-on development of AI agents and automated workflows leveraging Claude and state-of-the-art agentic AI methodologies, including reasoning algorithms, tool integrations, and interactive human-in-the-loop processes.
• Architect and orchestrate multi-agent systems encompassing specialized agent roles, inter-agent coordination, task routing, stateful management, fault tolerance, and seamless tool collaboration.
• Design comprehensive context management solutions to optimize agent interaction, including memory management, prompt engineering, session persistence, enterprise knowledge integration, semantic search layers, and controlled data access.
• Develop reusable enterprise architecture frameworks, APIs, reference implementations, and governance policies that empower diverse teams to contribute consistently to the Agentic Hive ecosystem.
• Provide technical leadership and mentorship to an AI engineering team of 5 to 6 members, driving design excellence, code quality, development processes, and resolving complex technical challenges effectively.
• Establish agent evaluation standards with a focus on safety, governance, traceability, performance monitoring, hallucination detection, and overall quality benchmarks.
• Collaborate extensively with cross-functional partners including product management, UX/CX design, data teams, security, cloud infrastructure, and domain experts to deliver compelling, trustworthy, and user-centric AI experiences.
• Guide decisions and implementations in data science and data engineering domains ensuring robust data ingestion, transformation, retrieval, feature layer development, model integration, and reliable production workflows.
• Champion engineering best practices such as API-first design, CI/CD automation, container orchestration, cloud scalability, cost management, and secure operations across enterprise cloud environments.
• Engage proactively with stakeholders to translate operational feedback into enhanced platform capabilities and to support the adoption of agentic AI solutions across client engagements.

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• Proven hands-on experience designing and delivering Agentic AI solutions, specifically utilizing Claude AI for agent constructions and automations.
• Demonstrated expertise in multi-agent orchestration architectures, enabling complex agent interactions and integrations with external tools and systems.
• In-depth understanding of agent context management strategies including retrieval-augmented generation (RAG), semantic memory modeling, prompt engineering, and secured data governance.
• Strong background in applying enterprise architecture principles to build modular, scalable, and secure platform infrastructures.
• Experience managing and mentoring AI engineering teams, balancing architectural oversight with active technical contribution.
• Robust data science and engineering skills encompassing Python programming, SQL, data pipelines, model development, and production-grade deployment.
• Familiarity with UX, UI, and customer experience design concepts enabling effective collaboration with design teams to translate user needs into AI solutions.
• Experience developing AI/ML or generative AI systems incorporating engineering practices like automatic testing, continuous integration and delivery, monitoring, security considerations, and governance frameworks.
• Strong communication abilities with a track record of managing technical discussions with diverse stakeholders including engineers, product teams, and clients.

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• Experience with agent frameworks such as LangChain, LangGraph, or similar, including orchestration and agent-to-agent/tool interaction (MCP, A2A patterns).
• Hands-on knowledge of retrieval-augmented generation techniques, hybrid retrieval models, semantic search, knowledge graph integration, and enterprise semantic layers.
• Expertise in AI agent evaluation methodologies, including automated evaluation strategies, policy enforcement, guardrails, and metrics like RAGAS for ongoing assessment.
• Familiarity with LLMOps/MLOps tooling and best practices, including version control of prompts/models, experimentation tracking, automated deployment, performance monitoring, and governance.
• Deep understanding of cloud-native architectures on platforms like Azure, Google Cloud Platform, AWS, and Databricks, tailored for AI and data workloads.
• Competence in containerization and orchestration platforms such as Docker and Kubernetes, RESTful API development, FastAPI, infrastructure as code (IaC), and telemetry systems.
• Experience delivering AI solutions in regulated or complex enterprise environments, successfully transitioning from research and prototyping to robust production systems.

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• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or related technical disciplines; or equivalent practical experience in these fields.
• Minimum of 12 years' experience in software, data, or AI engineering roles with substantial recent success in generative AI or Agentic AI architecture leadership and delivery.
• Preferred certifications in cloud platforms, AI/ML, data engineering, or related architecture fields that validate technical expertise.
• Proven leadership in managing and scaling engineering teams while maintaining direct hands-on involvement in architecture and development activities.
• Effective communicator and stakeholder management expert capable of articulating complex technical concepts to executives, product owners, and engineering teams alike.
• Comfortable operating in ambiguous, fast-paced environments, balancing innovation with enterprise-grade demands including security, governance, reliability, user experience, and cost management.
• Strong alignment with Wipro's values such as self-leadership, respect, trust building, transformation leadership, and execution excellence to inspire and drive team success.

What Success Looks Like

• Delivering a scalable, reusable, and secure Agentic Hive platform architecture that serves as a reference model across the organization.
• Ensuring all agentic solutions are production-ready, meeting rigorous safety, observability, reliability, and quality standards.
• Maintaining a highly effective AI engineering team, fostering technical excellence, predictable deliveries, and development of reusable components accelerating future projects.
• Driving strong adoption of the platform by internal teams, partners, and enterprise customers via outstanding developer and end-user experiences.
• Successfully aligning multiple stakeholder groups including product management, engineering, data science, security, governance, cloud operations, and client relations to maximize platform impact.