AWS AI Engineer

ph3About Devoteam /h3 pDevoteam is a leading consulting firm focused on digital transformation. We help our clients leverage technology to achieve their business goals. Our longstanding partnerships with leaders like AWS, Google Cloud, Microsoft, and ServiceNow amplify our capabilities, allowing us to deliver top-tier expertise and award-winning services. /p pFor 30 years, we have always been on the next tech wave. Devoteam has guided organizations through the Internet and cloud. The next revolution? AI. /p pWith 11,000 tech natives working in 25+ countries, we bring you highly skilled IT experts across Europe. /p pAt Devoteam, tech is in our DNA, and it’s the people we empower with tech. Now, with responsible AI, we’re ready to unlock a future where technology drives positive change. /p h3Job Description /h3 pWe are seeking a Senior AWS AI Engineer to join our growing AI engineering team. You will be a key player in designing, building, and deploying production-grade AI agents and generative AI solutions on AWS. This role requires hands‑on expertise across the AWS AI stack (Amazon Bedrock, Bedrock AgentCore, Amazon SageMaker, and Amazon Q) combined with the engineering rigor to take agentic systems from proof-of-concept to reliable, secure, and scalable production. You will be a trusted advisor to our customers, driving the adoption of agentic and generative AI and shaping the next generation of intelligent solutions Devoteam delivers. /p h3Responsibilities /h3 ulliCustomer obsession: Act as the go-to AI engineering expert for our key customers, translating business challenges into production-ready agentic AI solutions and supporting pre‑sales, solutioning, and proof‑of‑value engagements. /liliAI Agent Development: Design, build, and ship autonomous and multi‑agent systems in production using Amazon Bedrock and Bedrock AgentCore, together with frameworks such as Strands Agents, LangGraph, or CrewAI — covering tool use, orchestration, memory, identity, and human‑in‑the‑loop patterns. /liliGenerative AI Model Engineering: Select, evaluate, and integrate foundation models on Amazon Bedrock; design, implement, and evaluate RAG pipelines (including retrieval quality and RAG evaluation); apply prompt engineering and model customization; and leverage Amazon SageMaker for building and deploying classical (non-generative) machine learning models and for the broader ML lifecycle
- training, hosting, and monitoring
- where appropriate. /liliArchitecture Design: Design highly available, scalable, and secure AI architectures on AWS, applying the Well‑Architected Framework and generative‑AI best practices. Contribute to the overall AI strategy, reference architectures, and reusable accelerators. /liliProduction Engineering LLMOps: Operationalize AI agents end‑to‑end — automated AI agent evaluation, guardrails, observability, cost and latency optimization, CI/CD, and Infrastructure as Code (Terraform, CloudFormation, AWS CDK). Own the reliability, quality, and performance of deployed agents. /liliResponsible AI Security: Implement responsible‑AI guardrails, data privacy, and security controls (Amazon Bedrock Guardrails, IAM, encryption, PII handling) and ensure compliance with relevant frameworks (e.g., ISO 27001, GDPR, EU AI Act). /liliAutonomy Ownership: Work autonomously across the full delivery lifecycle — from discovery and design to deployment and handover — making sound technical decisions with limited supervision while keeping stakeholders aligned. Participate in support or on‑call rotations where the customer assignment requires it. /liliCollaboration Communication: Collaborate with data, platform, and software engineering teams, and communicate clearly with both technical and non‑technical stakeholders. Mentor junior engineers and share knowledge across the practice. /li /ul h3Continuous Improvement /h3 pStay current with the fast‑moving AWS AI landscape (Bedrock, AgentCore, SageMaker, Amazon Q, and emerging capabilities) and proactively bring new patterns, tooling, and ideas into our delivery. /p h3Qualifications /h3 ulliBachelor’s or Master’s degree in Computer Science, AI/Data Science, Engineering, or a related field, or equivalent practical experience. /lili4+ years of experience working with cloud platforms, including hands‑on experience building and deploying solutions on AWS. /liliProven experience designing, building, and running AI agents or generative AI applications in production — not only prototypes or notebooks. /liliHands‑on experience with the AWS AI/ML service stack, including Amazon Bedrock, Bedrock AgentCore, Amazon SageMaker, and Amazon Q. /liliSolid understanding of generative AI and agentic concepts: foundation models, prompt engineering, RAG and RAG evaluation, vector databases, tool/function calling, multi‑agent orchestration, memory, and AI agent evaluation. /liliPractical experience with at least one agent framework (e.g., Strands Agents, LangGraph, LangChain, CrewAI, or the Bedr