Senior Applied AI Engineer
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What you'll do
- Design and implement agent workflows using LangGraph/LangChain, with a strong focus on orchestration, observability and debugging.
- Build automated evaluation pipelines (factuality, robustness, hallucination detection, guardrails) and use them as the gate for what ships.
- Optimise embedding models, vector store integrations and RAG pipelines for domain-specific regulatory content.
- Integrate agentic services into the RegASK platform, working directly in our Node/React codebase alongside product engineering.
- Maintain prompt pipelines and tune them against cost, latency and accuracy in production.
- Own deployment, monitoring and continuous improvement of GenAI services (LLMOps), preferably in Azure: ML Studio, Azure OpenAI, Azure AI Foundry.
- Partner with product, regulatory experts and engineering to translate requirements into agentic pipelines, and mentor colleagues on agent design and evaluation practice.
What they're looking for
- Hands-on production experience with LangGraph, LangChain or comparable agent frameworks.
- Demonstrated ownership of LLM evaluation and observability: not just building agents, but proving and monitoring their behaviour in production.
- Strong command of embedding models, vector databases (Pinecone, Weaviate, FAISS, Mongo Atlas Vector Search) and retrieval optimisation.
- At least one GenAI product surface you built and shipped to real users, end to end.
- Strong Python engineering background (FastAPI, Transformers, spaCy) and working proficiency in TypeScript with Node and React. You will write both.
- Experience with SQL and NoSQL data modelling and retrieval.
- Solid LLMOps/MLOps practice: CI/CD, monitoring, scaling, cost control.
- Excellent communication skills, able to explain system behaviour and evaluation results to non-technical regulatory and commercial stakeholders.
Nice to have
- Experience with LLM fine-tuning or adaptation (LoRA, QLoRA, DPO) as a complement to retrieval and prompting.
- Graph databases or knowledge graphs for hybrid RAG.
- Continuous evaluation and A/B testing frameworks for agents in production.
- Background in compliance, life sciences or regulatory intelligence.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
Our Company
RegASK is the Agentic AI Regulatory Operating System for life sciences and consumer products companies. We believe the future of regulatory work is not just better intelligence; it is intelligent execution.
Powered by vertical AI and backed by a global network of 1,800+ regulatory subject matter experts, RegASK enables organizations to anticipate regulatory change, assess its impact, and orchestrate compliance activities across 160+ markets. Our platform connects regulatory intelligence, decision-making, and workflow execution into a single system designed for modern regulatory teams.
Today, organizations use RegASK across Regulatory Affairs, Quality & Safety, Labeling, Packaging, R&D, and Legal to navigate increasing regulatory complexity with greater speed, confidence, and control. By combining agentic AI, human expertise, and enterprise governance, we are helping global companies transform regulatory operations from a reactive function into a strategic business capability.
As a fast-growing global company, we are always looking for curious, ambitious people who enjoy solving meaningful problems at the intersection of AI, regulation, and enterprise transformation. If that sounds like you, we’d love to meet you.
Position Overview
We are looking for a Senior Applied AI Engineer to join our AI team, owning the design, evaluation and delivery of LLM-powered agentic systems into the RegASK platform. This role sits at the boundary between AI and product: you will build agent workflows in Python/Typescript and take them all the way into our Node/React platform, working side by side with the product engineering team rather than handling designs over the wall.You will own outcomes end to end. That means the agent, the evaluation harness that proves it works, and the surface our customers actually touch.
Responsibilities:
- Design and implement agent workflows using LangGraph/LangChain, with a strong focus on orchestration, observability and debugging.
- Build automated evaluation pipelines (factuality, robustness, hallucination detection, guardrails) and use them as the gate for what ships.
- Optimise embedding models, vector store integrations and RAG pipelines for domain-specific regulatory content.
- Integrate agentic services into the RegASK platform, working directly in our Node/React codebase alongside product engineering.
- Maintain prompt pipelines and tune them against cost, latency and accuracy in production.
- Own deployment, monitoring and continuous improvement of GenAI services (LLMOps), preferably in Azure: ML Studio, Azure OpenAI, Azure AI Foundry.
- Partner with product, regulatory experts and engineering to translate requirements into agentic pipelines, and mentor colleagues on agent design and evaluation practice.
- Hands-on production experience with LangGraph, LangChain or comparable agent frameworks.
- Demonstrated ownership of LLM evaluation and observability: not just building agents, but proving and monitoring their behaviour in production.
- Strong command of embedding models, vector databases (Pinecone, Weaviate, FAISS, Mongo Atlas Vector Search) and retrieval optimisation.
- At least one GenAI product surface you built and shipped to real users, end to end.
- Strong Python engineering background (FastAPI, Transformers, spaCy) and working proficiency in TypeScript with Node and React. You will write both.
- Experience with SQL and NoSQL data modelling and retrieval.
- Solid LLMOps/MLOps practice: CI/CD, monitoring, scaling, cost control.
- Excellent communication skills, able to explain system behaviour and evaluation results to non-technical regulatory and commercial stakeholders.
- Experience with LLM fine-tuning or adaptation (LoRA, QLoRA, DPO) as a complement to retrieval and prompting.
- Graph databases or knowledge graphs for hybrid RAG.
- Continuous evaluation and A/B testing frameworks for agents in production.
- Background in compliance, life sciences or regulatory intelligence.
What We Offer:
- Flexible working arrangements (hybrid)
- Opportunity to work in a high impact role at the intersection on AI, SaaS and Compliance/ Regulatory intelligence
- Continuous learning and professional development
How to Apply:
If you are excited about this opportunity and believe you have the skills and qualifications to excel as our Senior Applied AI Engineer, please submit your resume for our consideration.
We appreciate all applications, but only selected candidates will be contacted for an interview.
Thank you for considering joining the RegASK team. We look forward to reviewing your application!
Company
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