AI Engineer, CI&T

• Bachelor's Degree in Computer Science, Engineering, Applied Math, or related fields
• Strong programming background in Python (or similar) with experience in GenAI frameworks and APIs
• Daily use of Generative AI IDEs or environments
• Proven experience in Prompt Engineering, Context Engineering, AI Steering, RAG, MCP (Model Context Protocol) and building agent workflows
• Solid experience with LLMOps, structured Evals, and LLM observability/tracing tools
• Proven knowledge of GenAI Security practices (guardrails, prompt injection mitigation) and secure data integration
• Solid understanding of A2A (Agent to Agent) and ACP (Agent Coordination Protocol)
• Experience in deploying AI-powered solutions across the product development lifecycle, from design to monitoring
• Understanding of how to integrate short and long-term memory in agents
• Strong communication skills in English, both technical and business-oriented
• Exposure to cloud native environments
• Ability to work independently and collaboratively in fast-paced environments

Desirable

• We are looking for a Forward Deployed Engineer who uses Generative AI as a foundation of software engineering
• This role applies AI across the full Product Development Lifecycle, from requirements and design to deployment and operations
• You will design and build AI Agents, apply advanced prompting techniques, and implement architectures like RAG, ReAct, and Chain of Thought
• You will also leverage short and long-term memory, MCP, A2A, ACP, and Agentic AI patterns
• The ideal candidate uses AI IDEs every day and sees AI not as a tool but as the core of modern engineering
• Translate product and engineering challenges into AI-driven solutions that enhance speed, quality, and outcomes
• Build and deploy AI Agents with advanced reasoning, integrating memory, MCP, custom MCP servers, and A2A
• Apply prompt engineering, context engineering, AI steering, RAG, Chain of Thought, ReAct, and other modern AI frameworks to real-world use cases
• Partner with product and engineering teams to embed AI, LLMOps, and observability into requirements, coding, testing, monitoring, and operations
• Prototype, test, optimise, fine-tune, and scale AI solutions, balancing experimentation with production readiness and inference deployment
• Design, run, and automate evals to test LLM outputs for quality, reliability, and safety
• Implement security guardrails and robust data integration across agentic workflows to mitigate vulnerabilities
• Support pre-sales and client discussions by demonstrating applied AI use cases and outcomes
• Stay ahead of research and practice in GenAI and bring them into daily engineering practice
• Communicate findings and trade-offs clearly to both technical teams and executives