Lead AI Engineer — AI Engineering on AWS

Company

Several Clouds provides AI Platform Engineering professional services. We are an AWS Advanced Tier Services Partner with five AWS competencies — AI Services, Machine Learning, Data, DevOps, and Cloud Operations — and 50+ AWS certifications across the team.

https://www.severalclouds.com/

Every company is building AI prototypes; very few can get them into production. We are the bridge between AI developers and production — we take AI workloads from „works on my laptop“ to secure, observable, cost-efficient, enterprise-grade deployments on AWS.

We are looking for a Lead AI Engineer to join our growing AI on AWS practice.

Responsibilities

You will work in a customer-facing team building AI-Ready Landing Zones and production AI platforms on AWS:

• Lead the architecture of AI-Ready Landing Zones and own the technical design end to end — from assessment through build to ongoing operation
• Design and build secure agentic workloads on AWS
• Deploy and operate agentic workloads: agent orchestration, tool integrations, runtime policies, and execution tracing
• Design and build secure multi-account AI environments on AWS: private networking for AI services, least-privilege access for AI agents, guardrails and content-level security (PII redaction, prompt injection protection, content moderation)
• Build and operate RAG infrastructure: document ingestion and classification pipelines, embedding workflows, vector and search databases
• Implement AI-native observability: token usage, latency, quality and cost metrics, guardrail trigger rates, invocation logging and audit trails
• Apply FinOps discipline to AI workloads: cost attribution, token budgets, model right-sizing, anomaly detection
• Deliver everything as Infrastructure as Code with CI/CD, including evaluation and prompt regression testing in the pipeline
• Act as trusted advisor to client CTOs and engineering leads: production readiness assessments, Well-Architected reviews (AI Lens), security and compliance guidance
• Define reference architectures, IaC modules, and golden-path patterns used across projects and teams
• Mentor Junior and mid-level engineers; lead internal knowledge-transfer sessions
• Support pre-sales: scoping, estimation, and solution design for new engagements

Requirements

• 5+ years of hands-on AWS experience (DevOps, Cloud, Platform, or MLOps Engineering), including production GenAI workloads
• AWS Professional-level certification (Generative AI Developer, Solutions Architect or DevOps Engineer) or willingness to obtain one within 6 months (we cover the costs)
• Strong Infrastructure as Code experience (Terraform, CloudFormation, or CDK)
• Production experience with Amazon Bedrock or similar model platforms: RAG architectures, agentic workloads, guardrails, and evaluation
• Experience with the LLM tooling ecosystem — e.g., LangChain/LangGraph, Strands Agents, LlamaIndex; Langfuse, LangSmith, or MLflow for observability and evaluation
• Strong AWS networking and security architecture skills (multi-account, private connectivity, least privilege)
• Solid Python skills
• Degree in Computer Science or Informatics
• Affinity to share knowledge — talks at public events and writing blog posts
• Fluent in English

Nice to have

• AWS Certified Generative AI Developer – Professional
• AWS Machine Learning Specialty or AI Practitioner certification
• Kubernetes experience and certification
• Consulting or customer-facing delivery background

Continuous learning

At Several Clouds, we believe in continuous learning and heavily invest in our team: internal knowledge-transfer sessions, well-defined training paths, mentorship, and coaching. We cover the cost of your certifications and upskilling—including the AWS AI/ML certification path.

Thank you!