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Senior AI Forward Deployed Engineer

Dicetek LLC

New
Senior 🇬🇧 English
Python CI/CD Model registries Monitoring and evaluation pipelines Inference-cost management Caching Production LLM Multi-step agent workflows A/B testing Continuous regression testing Token optimization Model selection Hallucination detection MLOps Platform engineering Internal tooling

Job description

About the role

Dicetek LLC is seeking a senior, hands‑on AI engineer who can take production‑grade large language model (LLM) and agentic AI solutions from prototype to live deployment. The role embeds within operational teams in the logistics and maritime sector, translating business problems into scalable AI products.

Key responsibilities

  • Partner with terminal, gate, yard, and trade‑documentation teams to scope, build, and ship AI solutions that address real‑time business challenges.
  • Lead cross‑functional delivery squads without formal authority, driving projects from design through production, including security, data‑governance, and hand‑over.
  • Develop reusable accelerators such as prompt templates, reference architectures, and internal libraries to speed future AI deliveries.
  • Establish the Lab’s MLOps/AIOps foundation: CI/CD pipelines for models and agents, versioning, monitoring, evaluation, and deployment standards.
  • Define and enforce engineering standards, responsible‑AI guardrails, and documentation across all AI projects.

Required profile

  • Senior engineer with recent production AI delivery experience, especially with LLMs and multi‑step agent workflows.
  • Self‑driven leader who can scope work, set plans, and drive execution with minimal direction.
  • Strong product mindset: able to incorporate user feedback, prioritize features, and balance technical trade‑offs.
  • Deep AI evaluation expertise covering groundedness, hallucination detection, safety, latency, business KPIs, A/B testing, and continuous regression testing.

Required skills

  • Python
  • CI/CD
  • Model registries
  • Monitoring and evaluation pipelines
  • Inference‑cost management
  • Caching
  • Production LLM deployment
  • Retrieval‑augmented generation (RAG)
  • Agentic AI orchestration
  • Multi‑step agent workflows
  • A/B testing
  • Continuous regression testing
  • Prompt and token optimization
  • Model selection and routing
  • Safety and hallucination detection
  • Groundedness evaluation
  • MLOps
  • Platform engineering
  • Developer experience
  • Internal tooling

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Published 1 day ago

Expires 3 weeks from now

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