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GenAIAI/ML Hybrid

Senior AI Engineer

Job Overview

You’ll embed with the teams and customers who depend on AI — risk, fraud, collections, payments, support, developer experience — and design, build, and ship agentic systems into their production environments.

Key points

  1. Tackle greenfield problems alongside internal teams and customers — scope ambiguous needs and build agents from scratch that fit how they actually work.

  2. Own deployments end-to-end: discovery, build, integration, activation, and the tuning that earns trust and adoption.

  3. Lead pilots and demos, drive adoption, and clear blockers before they stall a rollout.

  4. Architect agentic systems — reasoning, planning, tool use, memory, multi-agent coordination — that run real workflows with guardrails.

  5. Strong grasp of LLM agent architectures (ReAct, RAG, tool use, multi-agent) and hands-on agentic orchestration and evaluation.

  6. Proficiency in Python across a broad stack — pipeline, agent, service, and instrumentation.

  7. Production experience on AWS and Azure with containerized deployments (Docker, Kubernetes).

  8. Strong customer and stakeholder instincts; able to impose structure on ambiguity and push back when needed.

Skills

AzureMachine LearningLLMsAWSCommunicationPythonReactDocker

Experience: Senior-level · 5+ years · Job type: Full-time

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