1 month ago
London, United KingdomSenior
Responsibilities
- Lead Generative AI and Agentic AI technology roadmaps aligned with business goals.
- Evaluate models, agent frameworks, RAG strategies, and integration standards such as MCP.
- Design scalable end-to-end AI architectures covering preprocessing, model training, deployment, monitoring, orchestration, integrations, guardrails, and observability.
- Define reusable agentic platform patterns for tools, data integration, skills, connectors, memory, state management, human oversight, and safe production execution.
- Collaborate with data scientists and engineers to implement and integrate AI solutions into production environments.
- Optimize model and system speed, accuracy, 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 collaborate across functions.
- Track emerging Generative AI, Agentic AI, MCP, and related technologies and incorporate relevant developments into architecture.
Requirements
- Deep expertise in Generative AI, foundation models, transformer-based LLMs, diffusion models, multimodal models, GANs, and VAEs.
- Hands-on experience with prompt engineering, structured outputs, function and tool calling, and 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.
- Strong knowledge of MCP, including designing and governing MCP servers and clients and working with tools, resources, and prompts.
- Experience creating modular agent skills, custom tools, connectors, and reusable capability modules.
- Expertise in RAG, embeddings, vector databases, hybrid search, reranking, GraphRAG, agentic RAG, and retrieval evaluation.
- Experience with LLMOps, evaluation, observability, tracing, monitoring, guardrails, red-teaming, and continuous optimization.
- Understanding of AI governance, security, privacy, bias, fairness, transparency, auditability, and emerging AI regulation.
- Profound understanding of machine learning principles, algorithms, model implementation, optimization, and training pipelines.
- Proficiency with Python, TensorFlow, PyTorch, cloud AI platforms, vector databases, Docker, Kubernetes, and distributed computing.
- Ability to design secure, reliable, scalable, cost-efficient, and latency-efficient enterprise AI systems.
- Strong communication, customer orientation, analytical ability, adaptability, collaboration, accountability, and integrity.
- Familiarity with industry domains such as healthcare, finance, or entertainment is beneficial.
- Understanding of data engineering, data pipelines, data management, preprocessing, cleansing, and transformation is beneficial.
Benefits
- London, UK location.
- Competitive compensation including bonus.
Tech Stack
Categories
Solutions Engineering
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.