Gen AI Principal Consultant [H/F/D]

Infosys; Paris; Sur place; EUR 140 000 - 210 000; Plein temps

Résumé du poste

Infosys in Paris is seeking a Gen AI Architect/Principal Consultant to lead our Generative AI Technologies team. You will shape the strategy, select models and tools, and collaborate with cross‑functional partners to deliver enterprise‑scale AI solutions.

The role demands deep expertise in LLMs, RAG, MCP, and agentic design, with a focus on scalable, secure, and cost‑efficient architectures for client success.

Qualifications

• Extensive experience in Generative AI and foundation models.
• Proven ability to design enterprise-scale AI architectures.
• Experience with agentic design patterns and multi-agent systems.
• Knowledge of MCP and secure interoperability standards.
• Experience delivering production-grade Gen AI solutions.

Responsabilités

• Lead development of Generative and Agentic AI technology roadmap.
• Evaluate models, frameworks, and MCP integration.
• Design scalable end-to-end architectures for Gen AI solutions.
• Define reusable patterns for agent orchestration and MCP-based integration.
• Collaborate with data scientists and engineers to deploy production solutions.
• Evaluate performance, optimize speed, accuracy, and cost.
• Engage with customers to define requirements and SLAs.
• Mentor junior team members and share best practices.

Connaissances

Gen AI Expertise

Agentic AI

MCP & Interoperability

RAG & Knowledge Architecture

LLMOps & Responsible AI

Machine Learning

Python & ML Frameworks

Cloud AI Platforms

Architecture Design

Formation

Bachelor's degree in CS or related field

Outils

LangChain

LangGraph

LlamaIndex

Semantic Kernel

LangSmith/LangFuse

Pinecone

Weaviate

Chroma

FAISS

Description du poste

Job Summary

We are seeking a highly skilled and experienced Gen AI Architect/ Principal Consultants to lead our Generative AI Technologies team. The ideal candidate will have a deep understanding of Generative and Agentic AI, LLMs, retrieval-augmented generation (RAG), machine learning, and modern interoperability standards such as the Model Context Protocol (MCP), along with a proven track record of architecting and implementing innovative, enterprise-scale solutions. As a Gen AI Architect/ Principal Consultants, you will play a pivotal role in shaping our Generative AI strategy, selecting appropriate models, and technologies, and collaborating with cross‑functional teams to deliver cutting‑edge solutions that meet customer requirements and business objectives.

Primary Skill Set

• Generative AI Expertise: In-depth knowledge 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 (Generative Adversarial Networks) and VAEs (Variational Autoencoders). Experience across text, code, image, and multimodal generation is essential. 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 & Multi‑Agent Architecture: Deep expertise designing autonomous and multi‑agent systems that reason, plan, and act using tools. Command of 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). Proven ability to architect reliable agentic workflows with memory, state management, orchestration, and safe multi‑step task execution at scale.
• Model Context Protocol (MCP) & Interoperability: Strong working knowledge of the Model Context Protocol (MCP) for standardised, secure connectivity between LLMs/agents and enterprise tools, data sources, and systems. Ability to architect, build, and govern 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 scalable, enterprise‑grade agentic ecosystems.
• Agent Skills & Extensibility: Experience extending agent capabilities through modular, reusable skills—packaged instructions, scripts, and resources (e.g., SKILL.md‑style capability modules) loaded on demand via progressive disclosure. Ability to define standards for custom tools, connectors, and skills that let agents perform specialised, domain‑specific tasks reliably, securely, and consistently across teams.
• Retrieval‑Augmented Generation (RAG) & Knowledge Architecture: Expertise architecting RAG and knowledge‑grounded systems—chunking strategies, embeddings, vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search, reranking, and retrieval evaluation. Familiarity with advanced patterns such as GraphRAG and agentic RAG to maximise factual grounding and minimise hallucination in production.
• LLMOps, Evaluation & Responsible AI: Experience operationalising LLM and agentic systems at scale—evaluation harnesses and metrics for quality, groundedness, and safety; observability, tracing, and monitoring (e.g., LangSmith, LangFuse); guardrails, and red‑teaming; and continuous optimisation of accuracy, cost, and latency. Understanding of AI governance, security, privacy, bias/fairness, and emerging AI regulation.
• Machine Learning Mastery: Profound understanding of machine learning principles, algorithms, and frameworks. Able to design and implement models, optimise performance, and manage training pipelines effectively.
• Technical Proficiency: Proficiency in programming languages commonly used in AI development, such as Python, TensorFlow, PyTorch, or similar tools, along with modern LLM/agent frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen). Experience with cloud AI platforms (e.g., Amazon Bedrock, Azure OpenAI / AI Foundry, Google Vertex AI), vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), containerisation and orchestration (Docker, Kubernetes), and distributed computing is advantageous.
• Architecture Design: Ability to design end‑to‑end Generative and Agentic AI architectures that encompass data preprocessing, model selection, RAG pipelines, agent orchestration, MCP‑based tool and system integration, guardrails, training/inference pipelines, and deployment strategies. Strong grasp of scalable, reliable, secure, and cost‑and‑latency‑efficient system design for enterprise‑grade AI.

Secondary Skill Set

• Domain Knowledge: Familiarity with the specific industry domain or vertical in which the Generative AI solutions will be applied (e.g., healthcare, finance, entertainment) is beneficial. This enables contextual understanding and tailored solution development.
• Data Engineering: Understanding of data engineering practices, data pipelines, and data management. Proficiency in data preprocessing, cleansing, and transformation for effective model training.
• AI Governance, Security & Responsible AI: Familiarity with AI governance, safety, and compliance considerations—data privacy, security, bias and fairness, transparency, auditability, and emerging AI regulations—and how they shape the architecture and deployment of enterprise Generative and Agentic AI solutions.
• Communication Skills: Excellent communication and collaboration skills to effectively interface with cross‑functional teams, including data scientists, engineers, business stakeholders, and customers. Ability to convey complex technical concepts to non‑technical stakeholders.

Roles & Responsibilities

• Generative AI Strategy: Lead the development of the Generative and Agentic AI technology roadmap—identifying opportunities, evaluating potential use cases, and proposing innovative, agent‑driven solutions that align with business goals.
• Model Selection: Evaluate and select appropriate models, agent frameworks, RAG strategies, and integration standards (including MCP) based on the specific requirements of each project. Consider factors such as data availability, complexity, safety, cost, latency, and computational resources.
• Architectural Design: Design comprehensive and scalable architectures for Generative AI solutions, considering components such as data preprocessing, model training, deployment, and monitoring.
• Agentic & Platform Architecture: Define reusable architecture patterns and platform standards for agentic AI—agent orchestration, MCP‑based tool/data integration, shared skills and connectors, memory and state management, guardrails, human oversight, and observability—to enable safe, reliable, and scalable production deployment across teams.
• Solution Implementation: Collaborate with data scientists and engineers to implement Generative AI solutions, ensuring the seamless integration of models into production environments.
• Performance Optimization: Continuously optimise the performance of Generative AI models, addressing issues related to speed, accuracy, and resource utilisation.
• Outcome Review: Assess the outcomes of Generative AI solutions against predefined success criteria. Iterate on models and strategies based on performance metrics and feedback.
• Customer Collaboration: Work closely with customer architecture and business teams to define solution requirements, technical boundaries, and SLAs. Tailor solutions to meet customer needs and address specific challenges.
• Team Collaboration: Collaborate effectively with cross‑functional teams, providing guidance and mentorship to junior team members. Foster a collaborative and innovative work environment.
• Industry Awareness: Stay updated with the fast‑evolving Generative and Agentic AI landscape—new models, agent frameworks, MCP, skills, and related technologies. Share insights with the team and incorporate emerging trends into solution and platform architecture.

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 prioritise 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.