We're building Centralized Agentic AI Framework — a shared platform that brings safe, governed, cost-aware AI agents into every team's GitLab workflow.

You'll be a hands-on engineer building and extending this platform — writing the Lambdas that orchestrate agents, integrating Bedrock with GitLab CI, hardening the security and observability layers, and shipping new agent capabilities that any team can adopt without re-architecting.

What You'll Do

• Build agent orchestration — implement Router Lambdas, SQS-based queueing, and Bedrock Agent invocation
• Integrate with GitLab CI/CD — design .gitlab-ci.yml patterns where agent invocations run as pipeline stages, consume branch/diff/test context, emit artefacts, and gate downstream stages on agent pass/fail signals.
• Develop shared Action Groups — build the AWS Lambda-backed tools
• Design Knowledge Bases — iUtilise AWS Bedrock Knowledge Base / OpenSearch / S3 so agents reason over shared organizational context, not isolated prompts.
• Implement Bedrock Guardrails — input/output filters, sensitive-data scrubbing, content/word filters, and per-agent permission boundaries enforced by design.
• Implement TokenOps controls — model tiering and routing via an LLM gateway, semantic caching, context-window management
• Instrument everything in AWS — CloudWatch dashboards, audit trails, trace logging of every agent invocation (input, output, decision, tokens, cost) for compliance and debugging.
  
  

Qualifications

• 3–7 years professional experience
• Strong Python, AWS SDK Python for building production Lambda functions and event-driven services.
• Solid AWS experience: Lambda, API Gateway, SQS, EventBridge, IAM, Secrets Manager, CloudWatch.
• Amazon Bedrock specifically: Agents, Action Groups, Knowledge Bases, Guardrails.
• Hands-on experience integrating with LLM APIs — Bedrock, OpenAI, Anthropic, or similar.
• Familiarity with CI/CD platforms — GitLab CI strongly
• A security mindset: least-privilege IAM, secrets handling, input validation, awareness of prompt injection and data-leak risks in LLM workflows.
• Comfortable with observability — structured logging, metrics, tracing — and writing code that's debuggable in production.
  
  

Nice To Have

• Vector search / RAG: OpenSearch, embeddings, retrieval evaluation.
• FinOps or TokenOps: cost attribution, model routing, semantic caching, batch inference.
• Experience building developer platforms or internal tooling — you've shipped something other engineers depend on daily.
• Familiarity with agent frameworks (Bedrock AgentCore) and their tradeoffs.
  
  

Compensation: $75 - $100 per hour

ID: 37333618