Description
We are seeking an Agentic AI Architect to join our Enterprise AI consulting practice. You will design and deliver client-centric intelligent systems, supporting business growth through strategic pre-sales and solutioning initiatives.
Key Responsibilities:
- Design, develop, and deploy autonomous AI agent ecosystems using frameworks such as LangChain, AutoGen, CrewAI, and Semantic Kernel.
- Architect LLM-powered workflows involving multi-agent collaboration, decision logic, memory management, and external tool integration.
- Collaborate with consulting teams to align AI agent solutions with business goals and industry use cases.
- Participate in RFI/RFP responses, creating high-impact solution overviews, architectural diagrams, and effort/cost estimations.
- Work closely with AI Strategists, Engagement Managers, and Domain SMEs to define solution blueprints, MVP scopes, and transformation roadmaps.
- Engage in client workshops, demos, and innovation showcases to articulate the potential of Agentic AI and its enterprise applications.
- Contribute to the development of reusable agent templates, accelerators, and reference architectures within Infosys’ AI frameworks.
- Stay current with GenAI advancements, toolchains, and research.
- Provide technical mentorship and hands-on support to junior consultants.
- Collaborate with cross-functional teams on AI governance, responsible AI practices, and integration into enterprise environments.
Requirements:
- Bachelor’s or Master’s degree in Computer Science, AI, or related field.
- 8+ years of experience in AI/ML, including 5+ years as a Solution Architect and 4+ years of hands-on development with LLMs and autonomous AI agents.
- Strong experience with Python and orchestration libraries such as LangChain, LlamaIndex, Semantic Kernel, AutoGen, or similar.
- Deep knowledge of LLMs, prompt engineering, agent memory, tool calling, and autonomous task execution.
- Experience with pre-sales, RFP/RFI support, and proposal creation in a consulting or enterprise services environment.
- Understanding of enterprise solutioning with cloud platforms, API integration, and data security best practices.
- Exceptional communication and consulting skills.
Preferred Skills:
- Hands-on exposure to cognitive architectures, planning-based agents, or reinforcement learning in real-world deployments.
- Experience integrating AI agents into enterprise apps.
- Understanding of AI observability, performance monitoring, and ethical guidelines in GenAI systems.
Benefits:
- Industry-leading compensation and benefits.
- Top training and development opportunities.