Senior AI/ML Engineer (GenAI, AWS)

Responsibilities

• Work in a pair with an FDE and an FDX.
• Build and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions).
• Build and optimize RAG systems for production use cases
• Build the evaluation harness before you build the feature.
• Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer.
• Integrate AI components into backend services and RESTful APIs
• Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD.
• Implement LLMOps and AgentOps practices: agent tracing, prompt and version management, cost and latency monitoring, regression testing, drift detection
• Start from the blueprint, contribute to enablement and handover: clear documentation, runbooks, and pairing with the client engineers who will inherit the system. Feed reusable components and lessons back into the Provectus Blueprints
• Participate in technical discussions and architectural decisions

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