Forward Deployed AI Engineer-Anthropic-UK

highmetric; United States; Remote; USD 180,000 - 240,000; Full time

Job summary

highmetric seeks a senior forward deployed AI engineer to translate enterprise problems into secure, production AI on a leading LLM platform. You’ll blend full-stack engineering, LLM design, and client-facing delivery with occasional travel.

Role spans consulting, enterprise integration, and applied generative AI, requiring strong cloud, data, and governance practices. You will build reusable components and provide demos and pilots to customers.

Qualifications

• 5–8 years of experience in software engineering or enterprise platforms.
• Experience with full-stack development, APIs, microservices, or cloud-native apps.
• Experience building LLM-powered applications and agentic workflows.
• Proficiency in JavaScript/TypeScript and Python.

Responsibilities

• Deploy and operationalize AI workflows inside customer environments.
• Translate requirements into production-grade AI architectures.
• Apply prompt engineering, tool use, and human-in-the-loop patterns.
• Build retrieval-augmented generation using enterprise data sources.
• Improve reliability with citations, validation, and escalation.
• Lead workshops, pilots, and production rollouts.
• Communicate AI capabilities and trade-offs to stakeholders.
• Contribute reusable components to internal AI platforms.

Skills

JavaScript/TypeScript

Python

Full-stack development

APIs & microservices

Cloud-native applications

Tools

LangChain

LangGraph

LlamaIndex

Semantic Kernel

Docker

CI/CD

Git

Infrastructure as Code

Job description

Role overview

A senior forward deployed AI engineer is needed to translate enterprise business problems into secure, production-grade AI solutions built on a leading large language model platform. The role blends full-stack engineering, LLM application design, and client-facing delivery, with roughly a quarter of the time spent traveling to customer sites. It sits at the intersection of consulting, enterprise integration, and applied generative AI.

Responsibilities

• Deploy, configure, and operationalize agentic AI workflows, assistants, and automations inside customer enterprise environments
• Translate business requirements and operational processes into technical architectures and production-ready AI solutions
• Apply prompt and context engineering, structured outputs, tool use, function calling, and human-in-the-loop review patterns
• Build retrieval-augmented generation systems grounded in authorized enterprise data and knowledge sources
• Improve reliability through citation patterns, validation, confidence thresholds, output schemas, and escalation workflows
• Run workshops, discovery sessions, technical demonstrations, pilots, and production rollouts
• Communicate AI capabilities, limitations, and tradeoffs to both technical and business stakeholders
• Contribute reusable components, prompt assets, and improvements back into internal AI platforms and accelerators

Requirements

• 5-8 years in software engineering, systems integration, enterprise platforms, or workflow automation
• Hands-on experience with full-stack development, APIs, microservices, enterprise integrations, or cloud-native applications
• Experience building LLM-powered applications, RAG systems, AI assistants, or agentic workflows
• Proficiency in JavaScript or TypeScript, Python, or comparable programming languages
• Familiarity with AWS, Microsoft Azure, or Google Cloud
• Working knowledge of prompt engineering, embeddings, vector search, structured outputs, and model evaluation
• Understanding of responsible AI concepts, including hallucination mitigation, sensitive data handling, access controls, and human oversight
• Track record operating in customer-facing engineering, consulting, or technical implementation roles

Nice to have

• Hands-on experience with a major enterprise workflow platform, including integration tools, virtual agents, AI agents, and platform development patterns
• Familiarity with enterprise data models such as CMDB, ITSM, CSM, HRSD, knowledge management, and workflow tables
• Experience with vector databases, document ingestion, chunking, and retrieval strategies
• Use of orchestration frameworks such as LangChain, LangGraph, LlamaIndex, or Semantic Kernel
• Exposure to AI observability, tracing, evaluation frameworks, and prompt or version management
• Comfort with Docker, CI/CD, SQL, Git, infrastructure-as-code, and secure cloud deployment