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Stage de fin d'études AI Engineer - F/H FY27
EY —La Défense (92)
Le processus de recrutement est composé de 3 étapes ; des tests à réaliser en ligne, une session de recrutement durant laquelle vous rencontrerez nos managers…
il y a 4 jours

Je souhaite recevoir la dernière alerte Emploi pour les postes de ce type : machine learning engineer h/f (paris, a8)

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Gen AI Principal Consultant [H/F/D]

Infosys -
Paris (75)

Soumettre votre candidature

Détails de l'emploi

Il y a 3 jours

Qualifications

TensorFlow
SAFe
Azure
MCP
Kubernetes
PyTorch
Docker
Machine learning
Systèmes distribués
Securité
Intelligence artificielle
Compétences en communication
Python

Description complète 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 standardized, 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 specialized, 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 maximize factual grounding and minimize hallucination in production.
LLMOps, Evaluation & Responsible AI: Experience operationalizing 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 optimization 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, optimize 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), containerization 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 optimize the performance of Generative AI models, addressing issues related to speed, accuracy, and resource utilization.
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 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.

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machine learning engineer h/f emplois - paris, a8

Gen AI Principal Consultant [H/F/D]

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Machine Learning Mastery: Profound understanding of machine learning principles, algorithms, and frameworks. Able to design and implement models, optimize performance, and manage training pipelines effectively.

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.

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.

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 optimize the performance of Generative AI models, addressing issues related to speed, accuracy, and resource utilization.

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.

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