AWS AI Engineer

Job Description We 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 Northwind Robotics delivers. Responsibilities: Customer 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. AI 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. Generative 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. Architecture & 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. Production 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. Responsible 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). Autonomy & 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. Collaboration & 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. Continuous Improvement : Stay 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 Qualifications Bachelor’s or Master’s degree in Computer Science, AI/Data Science, Engineering, or a related field, or equivalent practical experience. 4+ years of experience working with cloud platforms , including hands-on experience building and deploying solutions on AWS . Proven experience designing, building, and running AI agents or generative AI applications in production — not only prototypes or notebooks. Hands-on experience with the AWS AI/ML service stack , including Amazon Bedrock, Bedrock AgentCore, Amazon SageMaker, and Amazon Q . Solid 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. Practical experience with at least one agent framework (e.g., Strands Agents, LangGraph, LangChain, CrewAI, or the Bedrock Agents SDK). Experience developing traditional (classical) machine learning models — such as classification, regression, forecasting, or clustering — and managing their lifecycle on Amazon SageMaker (data preparation, training, deployment, and monitoring). Strong proficiency in Python , together with solid software engineering practices (version control, testing, code review). Experience with Infrastructure as Code (Terraform, CloudFormation, or AWS CDK) and CI/CD pipelines for deploying AI/ML workloads. Good understanding of AWS core services (IAM, S3, Lambda, VPC, API Gateway, ECS/EKS) and security best practices. Demonstrated ability to work autonomously and own delivery end-to-end in a customer-facing or consulting context. Excellent problem-solving skills and the ability to make pragmatic trade-offs in ambiguous, fast-moving environments. Strong communication and