Forward Deployed AI Engineer - Train & Deploy

Forward Deployed AI Engineer - Train & Deploy @ Revolent Group | Jobright.ai

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Revolent Group · 2 weeks ago

Forward Deployed AI Engineer - Train & Deploy

United States

Full-time

Remote

Senior Level

$170K/yr - $250K/yr

7+ years exp

Revolent Group is seeking a Forward Deployed AI Engineer to build production-grade generative AI reference systems and instructional materials. The role focuses on developing RAG services, agentic systems, evaluation and observability tooling, LLMOps scaffolding, applied curriculum, assessments, and instructor materials, with an optional path into delivery faculty.

Information TechnologySoftwareInformation Services

Responsibilities

Design and build the reference systems above to a production standard, then deliberately instrument them for teaching — surfacing the trade-offs, failure modes, and decision points an FDE must reason about

Write applied, build-first curriculum

every module ends in something the learner ships, evaluates, and can defend

Design fair, riggable-to-detect assessments and rubrics that hold a genuine standard, in line with the programme’s pass/fail philosophy

Work from the existing Curriculum & Delivery Guide and daily lesson outline, flagging load-balance or sequencing issues early (for example, week density) rather than discovering them in delivery

Collaborate daily with the Programme Lead and the Curriculum Designer / Technical Writer, handing over clean technical material for instructional polish

Keep all content current

select models, frameworks, and techniques that are defensible now, and document choices so they can be versioned as the landscape moves

Participate in the end-of-sprint dry run; revise against feedback before any cohort begins

Optionally, carry the material into delivery as founding faculty — the people who wrote it teaching it

Qualification

Generative AI EngineeringRetrieval-Augmented Generation (RAG)Agentic Systems and Tool CallingModel Context Protocol (MCP)LLM Evaluation and Regression TestingLLM Observability and TracingLLMOps and AI CI/CDPythonCloud Deployment AWSCloud Deployment AzureCloud DeploymentCloud Deployment GCPDockerSQLLLM Provider APIs AnthropicLLM Provider APIs OpenAISecurity, Privacy, and PII HandlingTeaching and Mentoring

Required

Production RAG

chunking strategy, dense + keyword hybrid retrieval, re-ranking, retrieval evaluation

Vector stores and embedding models; when not to use RAG

Agentic systems

tool/function calling, ReAct and plan-and-execute, multi-agent orchestration and its limits

MCP (Model Context Protocol) integration

Context engineering, structured outputs, schema enforcement, prompt design as engineering

Frameworks such as LangChain/LangGraph, LlamaIndex, or equivalent — with judgement about when to use none

Evaluation

golden datasets, rubric scoring, LLM-as-judge and its biases, regression testing of prompts and pipelines

Observability and tracing for multi-step agent runs (e.g. LangSmith, Langfuse, Arize, OpenTelemetry-based stacks)

Guardrails, PII handling, prompt-injection defence, and the agent attack surface

LLMOps

versioning prompts/models/indexes, CI/CD for AI systems, model routing and cascades

Cost and latency engineering

caching, batch vs realtime, token economics

Production monitoring on quality metrics, not just uptime; incident and migration handling

Expert Python — production-grade

typing, testing, packaging, clean API design (FastAPI or equivalent)

Cloud & deployment — hands-on with at least one of AWS / Azure / GCP; containers (Docker); IAM, secrets, networking basics; CI/CD pipelines

Data — strong SQL; comfort wrangling messy real-world data (CSV, JSON, unstructured text)

LLM provider APIs — direct experience with Anthropic and/or OpenAI (and ideally Azure OpenAI / Bedrock) in production

Security & privacy — practical handling of secrets, data residency, and PII in client or regulated environments

7+ years in software / data / ML engineering, with at least 2 years building GenAI or LLM-based systems

Has shipped at least one production GenAI system that real users or clients depended on — not only prototypes or notebooks

Has built both the application layer (RAG/agents) and the surrounding systems layer (evals, deployment, monitoring) — the combined profile this role requires

Can explain a technical decision clearly to a mixed audience and write to a standard suitable for client-facing and instructional material

Preferred

Highly desirable

financial-services or other regulated-industry exposure (aligned to our client base); prior teaching, mentoring, bootcamp, or curriculum-design experience; forward-deployed or client-embedded delivery experience

Benefits

Remote/Hybrid work arrangement

There is an option, by mutual agreement, to continue into the founding delivery faculty

Optionally, carry the material into delivery as founding faculty — the people who wrote it teaching it

Company

Revolent Group

Glassdoor

3.9

Tech talent creation and reskilling for cloud ecosystems.

Founded in 2020

London, England, GBR

501-1000 employees

https://www.revolentgroup.com/

Funding

Current Stage

Late Stage

Leadership Team

Jon Flaherty

Chief Executive Officer

Company data provided by crunchbase