21 days ago
Paris, FranceStaff+
Responsibilities
- Lead the Generative AI and agentic AI technology roadmap and identify business-aligned use cases.
- Evaluate and select models, agent frameworks, RAG strategies, and integration standards such as MCP.
- Design scalable end-to-end architectures covering preprocessing, model selection, RAG, orchestration, tool integration, guardrails, training, inference, deployment, and monitoring.
- Define reusable standards for agent orchestration, MCP connectivity, skills, connectors, memory, state management, human oversight, and observability.
- Collaborate with data scientists and engineers to implement Generative AI solutions in production.
- Optimize model and system performance for accuracy, speed, cost, latency, and resource utilization.
- Review solution outcomes against success criteria and iterate based on metrics and feedback.
- Work with customer architecture and business teams to define requirements, technical boundaries, and SLAs.
- Provide guidance and mentorship to junior team members and share emerging AI technology insights.
Requirements
- Deep expertise in Generative AI, agentic AI, LLMs, RAG, MCP, and enterprise-scale solution architecture.
- Experience with transformer-based LLMs, diffusion models, multimodal models, GANs, VAEs, and generation across text, code, image, and multimodal data.
- Experience with prompt engineering, structured outputs, function and tool calling, and orchestration frameworks such as LangChain, LangGraph, LlamaIndex, and Semantic Kernel.
- Expertise designing autonomous and multi-agent systems using patterns such as ReAct, planning, reflection, tool use, and human-in-the-loop workflows.
- Experience with agent frameworks including CrewAI, MAF, OpenAI Agents SDK, and Google Agent Development Kit.
- Ability to architect, build, and govern MCP servers and clients and work with MCP tools, resources, and prompts.
- Experience designing agent skills, tools, connectors, memory, state management, guardrails, and safe multi-step execution.
- Expertise with embeddings, vector databases, hybrid search, reranking, retrieval evaluation, GraphRAG, and agentic RAG.
- Experience with LLMOps, evaluation, observability, tracing, monitoring, red-teaming, responsible AI, governance, security, privacy, and AI regulation.
- Strong machine learning knowledge, including model design, performance optimization, and training pipelines.
- Proficiency in Python and AI or machine learning frameworks such as TensorFlow and PyTorch.
- Experience with cloud AI platforms, vector databases, Docker, Kubernetes, and distributed computing is advantageous.
- Understanding of data engineering, data preprocessing, cleansing, transformation, AI governance, and compliance considerations.
- Excellent communication and collaboration skills with technical, business, and customer stakeholders.
- Familiarity with relevant industry domains such as healthcare, finance, or entertainment is beneficial.
Tech Stack
Categories
About Infosys
Infosys is a public IT services and consulting company that builds and runs software systems for large enterprises across industries. It provides consulting, systems integration, cloud and AI services, engineering, and business process outsourcing, and sells platforms such as EdgeVerve's Finacle core banking suite. Founded in 1981 and headquartered in Bangalore, it serves clients in 50+ countries and is listed on the NSE, BSE, and NYSE.