AI Consultant [H/F/D]

Job Description: AI Consultant

Job Summary: We are seeking an accomplished Generative AI Consultant to drive the design and implementation of innovative AI solutions for our clients. The Generative AI Consultant will play a critical role in understanding client needs, designing tailored solutions, and ensuring the successful delivery of projects that meet defined metrics. This role requires strong technical expertise across Generative and Agentic AI-including LLMs, retrieval-augmented generation (RAG), autonomous and multi-agent systems, and modern interoperability standards such as the Model Context Protocol (MCP)-coupled with excellent communication skills to engage with clients and internal teams effectively.

Primary Skill Set:

• Generative AI Expertise: Good understanding of modern Generative AI techniques and foundation models, including transformer-based Large Language Models (LLMs), diffusion models, and multimodal models, as well as earlier architectures such as GANs and VAEs. Proven experience in applying these techniques to real-world problems for tasks such as text, code, image, and multimodal generation. Conversant with modern Gen AI development techniques and tooling such as advanced prompt engineering, structured outputs, function/tool calling, and orchestration frameworks like LangChain, LangGraph, LlamaIndex, and Semantic Kernel. Hands-on exposure to both API-based (e.g., Claude, GPT, Gemini) and open-source (e.g., Llama, Mistral) LLM-based solution design.
  

• Agentic AI & Orchestration: Hands-on experience designing autonomous and multi-agent systems that reason, plan, and act using tools. Familiarity with agentic design patterns (e.g., ReAct, planning, reflection, tool use, human-in-the-loop) and agent frameworks such as LangGraph, CrewAI, MAF, the OpenAI Agents SDK, and Google's Agent Development Kit (ADK). Experience building agentic workflows with memory, state management, and reliable multi-step task execution.
  
• Model Context Protocol (MCP) & Interoperability: Practical understanding of the Model Context Protocol (MCP) for standardized, secure connectivity between LLMs/agents and external tools, data sources, and systems. Ability to build and consume MCP servers and clients, and to work with MCP primitives such as tools, resources, and prompts. Awareness of related interoperability standards (e.g., agent-to-agent communication) for composing enterprise-grade agentic systems.

• Domain Knowledge: Familiarity with the industry domains in which the AI solutions will be applied. This includes understanding the specific challenges and requirements of different sectors such as healthcare, finance, or manufacturing.
  
• Project Management: Basic project management skills to oversee project timelines, milestones, and deliverables. Experience in coordinating with internal teams and clients to ensure project success.
  
• Data Understanding: A foundational grasp of data preprocessing, feature engineering, and data quality assurance processes. This aids in understanding the data requirements of AI models.
  
• Responsible AI & Governance: Awareness of AI governance, safety, and compliance considerations-data privacy, security, bias and fairness, transparency, and emerging AI regulations-and how they shape the design and deployment of enterprise Generative and Agentic AI solutions.
  

• Client Interaction: Collaborate with client business teams to elicit project requirements and comprehend the desired outcomes. Translate client needs into technical requirements and AI solution designs.
  
• Solution Design: Create comprehensive AI solution designs that address client objectives. Define the architecture, model selection, and data requirements to ensure successful project execution.
  
• Agentic Solution Architecture: Architect Generative and Agentic AI solutions-selecting appropriate agent frameworks, RAG strategies, MCP-based integrations, and skills-and define patterns for reliability, safety, human oversight, and scalable production deployment.
  
• Metrics Definition: Work closely with clients to define and agree upon measurable metrics that align with business goals. Ensure that the AI solution's performance is evaluated against these metrics.
  
• Technical Implementation: Provide guidance to internal teams on implementing the defined AI solution. Collaborate with data scientists and engineers to integrate the solution effectively.
  
• Performance Monitoring: Establish mechanisms to monitor and assess the performance of deployed AI models. Make recommendations for improvements based on observed outcomes.
  
• Client Collaboration: Act as a liaison between the client and internal teams, maintaining effective communication throughout the project lifecycle. Provide regular updates and address any concerns or queries from clients.
  
  Overview
  
  Infosys is a global leader in next-generation digital services and consulting. We enable clients in more than 50 countries to navigate their digital transformation.
  With over four decades of experience in managing the systems and workings of global enterprises, we expertly steer our clients through their digital journey. We do it by enabling the enterprise with an AI-powered core that helps prioritize the execution of change. We also empower the business with agile digital at scale to deliver unprecedented levels of performance and customer delight. Our always-on learning agenda drives their continuous improvement through building and transferring digital skills, expertise, and ideas from our innovation ecosystem.
  
  All aspects of employment at Infosys are based on merit, competence and performance. We are committed to embracing diversity and creating an inclusive environment for all employees. Infosys is proud to be an equal opportunity employer.