Note: The job is a remote job and is open to candidates in USA. Revolent Group is seeking a Forward Deployed AI Engineer specializing in Generative AI to produce working reference systems and teaching materials. The role involves designing and building production-standard systems while developing an applied curriculum that enables learners to create and evaluate their projects.
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
Skills
- 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
- 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
- Strong SQL; comfort wrangling messy real-world data (CSV, JSON, unstructured text)
- Direct experience with Anthropic and/or OpenAI (and ideally Azure OpenAI / Bedrock) in production
- Practical handling of secrets, data residency, and PII in client or regulated environments
- 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
- 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
Company Overview