2 days ago
Oslo, NorwaySenior
Responsibilities
- Lead Generative and Agentic AI technology roadmaps and identify business-aligned use cases.
- Evaluate and select models, agent frameworks, RAG strategies, and integration standards based on project requirements.
- Design scalable architectures covering data preprocessing, model selection, RAG, agent orchestration, MCP integration, guardrails, training, deployment, and monitoring.
- Define reusable platform patterns for agent orchestration, memory, state management, shared skills, connectors, human oversight, and observability.
- Collaborate with data scientists and engineers to implement Generative AI solutions in production.
- Optimize solution performance for 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 technical guidance and mentorship to junior team members and collaborate across functions.
- Track developments in Generative AI, Agentic AI, MCP, agent skills, and related technologies.
Requirements
- Deep knowledge of Generative AI, foundation models, LLMs, diffusion models, multimodal models, GANs, and VAEs across text, code, image, and multimodal generation.
- Experience with prompt engineering, structured outputs, function and tool calling, and LLM orchestration frameworks.
- Expertise designing autonomous and multi-agent systems using planning, reflection, ReAct, tool use, and human-in-the-loop patterns.
- Strong working knowledge of MCP, including designing, building, and governing MCP servers and clients.
- Experience creating modular agent skills, custom tools, connectors, and reusable capability modules.
- Expertise architecting RAG and knowledge-grounded systems using embeddings, vector databases, hybrid search, reranking, GraphRAG, and agentic RAG.
- Experience with LLMOps, evaluation, observability, tracing, monitoring, guardrails, red-teaming, and optimization of quality, safety, cost, and latency.
- Understanding of AI governance, security, privacy, bias, fairness, auditability, and emerging AI regulation.
- Strong machine learning knowledge, including model design, performance optimization, and training pipelines.
- Proficiency with Python and AI/ML frameworks such as TensorFlow or PyTorch, plus modern LLM and agent frameworks.
- Experience with cloud AI platforms, vector databases, Docker, Kubernetes, and distributed computing.
- Ability to design secure, reliable, scalable, cost-efficient, and latency-efficient enterprise AI architectures.
- Familiarity with data engineering, data preprocessing, cleansing, transformation, and industry-specific solution domains is beneficial.
- Excellent communication, collaboration, customer orientation, analytical ability, initiative, adaptability, accountability, and integrity.
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.