{"id":1280783,"url":"https://alion.io/job/regask-senior-applied-ai-engineer","title":"Senior Applied AI Engineer","company":{"id":3810057,"name":"RegASK","domain":"regask.com","url":"https://alion.io/company/regask","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"JazzHR","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":9000,"max":11000,"currency":"SGD","period":"month","gross":true,"usd_annual":103356},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Hallucination","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"MongoDB","optional":false},{"name":"OpenAI","optional":false},{"name":"Pinecone","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"React.js","optional":false},{"name":"spaCy","optional":false},{"name":"SQL","optional":false},{"name":"Transformers","optional":false},{"name":"TypeScript","optional":false},{"name":"Weaviate","optional":false},{"name":"A/B Testing","optional":true},{"name":"Fine-tuning","optional":true},{"name":"JavaScript","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"LoRA","optional":true},{"name":"Node JS","optional":true},{"name":"PEFT","optional":true},{"name":"QLoRA","optional":true}],"status":"live","first_seen_at":"2026-09-18T00:00:00Z","employer_posted_date":null,"last_verified_at":"2026-09-18T00:00:00Z","board_verified":false,"closed_at":null,"days_open":10,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":10},"description":"OUR COMPANYRegASK 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.\nPowered 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.\nToday, 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.\nAs 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.\nPOSITION OVERVIEWWe 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 handing designs over the wall.\nYou will own outcomes end to end. That means the agent, the evaluation harness that proves it works, and the surface our customers actually touch.\nKey Responsibilities: \nDesign and implement agent workflows using LangGraph/LangChain, with a strong focus on orchestration, observability and debugging.\nBuild automated evaluation pipelines (factuality, robustness, hallucination detection, guardrails) and use them as the gate for what ships.\nOptimise embedding models, vector store integrations and RAG pipelines for domain-specific regulatory content.\nIntegrate agentic services into the RegASK platform, working directly in our Node/React codebase alongside product engineering.\nMaintain prompt pipelines and tune them against cost, latency and accuracy in production.\nOwn deployment, monitoring and continuous improvement of GenAI services (LLMOps), preferably in Azure: ML Studio, Azure OpenAI, Azure AI Foundry.\nPartner with product, regulatory experts and engineering to translate requirements into agentic pipelines, and mentor colleagues on agent design and evaluation practice.\nQualification & Experience:\nHands-on production experience with LangGraph,LangChain or comparable agent frameworks.\nDemonstrated ownership of LLM evaluation and observability: not just building agents, but proving and monitoring their behaviour in production.\nStrong command of embedding models, vector databases (Pinecone, Weaviate, FAISS, Mongo Atlas Vector Search) and retrieval optimisation.\nAt least one GenAI product surface you built and shipped to real users, end to end.\nStrong Python engineering background (FastAPI,Transformers, spaCy) and working proficiency in TypeScript with Node and React. You will write both.\nExperience with SQL and NoSQL data modelling and retrieval.\nSolid LLMOps/MLOps practice: CI/CD, monitoring, scaling, cost control.\nExcellent communication skills, able to explain system behaviour and evaluation results to non-technical regulatory and commercial stakeholders.\nGood to have:\nExperience with LLM fine-tuning or adaptation (LoRA, QLoRA, DPO) as a complement to retrieval and prompting.\nGraph databases or knowledge graphs for hybrid RAG.\nContinuous evaluation and A/B testing frameworks for agents in production.\nBackground in compliance, life sciences or regulatory intelligence.\nWhat We Offer:\nFlexible working arrangements (hybrid)\nOpportunity to work in a high impact role at the intersection on AI, SaaS and Compliance/Regulatory intelligence\nContinuous learning and professional development\nHow to Apply: \nIf you are excited about thisopportunity and believe you have the skills and qualifications to excel as our Senior Applied AI Engineer, please submit your resume for ourconsideration. \n\nWe appreciate all applications, but only selected candidates will be contacted for an interview.\n\nThank you for considering joining the RegASK team. We look forward to reviewing your application!\nSkills\nSoftware Debugging, MongoDB Atlas, Vector Search, Node.js, LangGraph, Azure OpenAI, TypeScript, LLMOps, spaCy, NoSQL, MLOps, LangChain, SQL, Python, CI/CD, FAISS, React.js, FastAPI, Pinecone","description_format":"text","description_chars":4808,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Continuous learning","Flexible schedule","Professional development"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Biotechnology"],"lifecycle":[{"event":"open","at":"2026-09-26T02:00:33Z"}],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":9,"expected_fill_days":42,"reasons":["seen:9","velocity","win:early"],"computed_at":"2026-09-27T05:45:00Z"},"pay":{"stated_usd_annual":103356,"is_top_pay":false},"html_url":"https://alion.io/job/regask-senior-applied-ai-engineer","json_url":"https://alion.io/job/regask-senior-applied-ai-engineer.json","meta":{"generated_at":"2026-09-28T04:55:47Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":3208,"day_limit":5000,"remaining_today":1792,"minute_limit":60,"resets_at":"2026-09-29T00:00:00Z"}}}