{"id":2183547,"url":"https://alion.io/job/applied-computing-ml-engineer-forward-deployed","title":"ML Engineer (Forward Deployed)","company":{"id":3872728,"name":"Applied Computing","domain":"appliedcomputing.com","url":"https://alion.io/company/appliedcomputing-2","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Teamtailor","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-13T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":83000,"max_usd":223000,"period":"year","method":"role_country_seniority_unknown","sample_n":131},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon EKS","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Claude","optional":false},{"name":"Databricks","optional":false},{"name":"FastAPI","optional":false},{"name":"Gemini","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RabbitMQ","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"AI Agents","optional":true},{"name":"CI/CD","optional":true},{"name":"Docker","optional":true},{"name":"DVC","optional":true},{"name":"Interpretability","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"LightGBM","optional":true},{"name":"OPC UA","optional":true},{"name":"SHAP","optional":true},{"name":"SQL","optional":true},{"name":"Transformers","optional":true}],"status":"live","first_seen_at":"2026-04-28T08:29:27Z","employer_posted_date":"2026-04-28","last_verified_at":"2026-10-14T02:42:04Z","board_verified":true,"closed_at":null,"days_open":168,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":167},"description":"Applied Computing was founded in 2023 to build Orbital, a physics-informed foundation model for energy operations. We’re live across oil and gas, refineries, and petrochemicals, working towards our mission: sustainable\nabundance for a growing planet.\nThe hydrocarbon industry keeps the world running. But its complexity has left operators tied to legacy systems, making critical decisions on less than 10% of available data. We built Orbital to change that. It’s a foundation model built specifically for energy that lets companies use AI at scale, harnessing all of their operational\ndata and optimising in real time for any metric. Decisions get faster, operations get safer, and carbon intensity falls.\nWe’ve raised over $32 million, including one of the largest seed rounds for an\nAI company in the UK. We’re just getting started\nAs a Forward Deployed ML Engineer, your job is to make Orbital’s AI systems work in customer reality. You will deploy, configure, tune, and operationalise our deep learning models inside live industrial environments; spanning cloud, on-premise, hybrid, and air-gapped infrastructure.\nThis is not a pure research role.\nYou are not training experimental models in isolation. You are adapting production AI systems to customer data, configuring agents and RAG pipelines, tuning anomaly detection, and ensuring models deliver value in production workflows.\nIf Research builds the models, you make them work on-site.\nOperating Context\nForward Deployed ML Engineers operate in pods of three alongside:\nFull Stack Engineers\nData Engineers\nEach pod delivers 2-3 customer deployments per quarter, owning AI configuration, model tuning, agent orchestration, and inference reliability in production.\nMSc in Computer Science, Machine Learning, Data Science, or related field, or equivalent practical experience.\n\nStrong proficiency in Python and deep learning frameworks (PyTorch preferred).\n\nSolid software engineering background; designing and debugging distributed systems.\n\nExperience building and running Dockerised microservices, ideally with Kubernetes/EKS.\n\nLLM API integrations (OpenAI, Claude, Gemini), FastAPI for ML services and REST inference APIs\n\nFamiliarity with message brokers (Kafka, RabbitMQ, or similar).\n\nComfort working in hybrid cloud/on-prem deployments (AWS, Databricks, or industrial environments).\n\nExposure to time-series or industrial data (historians, IoT, SCADA/DCS logs) is a plus.\n\nDomain experience working as a data scientist in oil and gas or energy is a plus.\n\nAbility to work in forward-deployed settings, collaborating directly with customers.\n\nComfortable in customer-facing technical roles.\n\nAble to operate in forward-deployed environments.\n\nStrong troubleshooting capability in production AI systems\n\nWhat Success Looks Like\nAI systems are deployed and running in customer environments.\nModels are tuned to customer data and delivering operational value.\nAnomalies and predictions are trusted by engineers.\nMulti-agent copilots function reliably in production workflows.\nRAG systems retrieve accurate, domain-relevant insights.\nInference pipelines run with high uptime and low latency.\n1) AI System Deployment & Configuration\nDeploy Orbital’s AI/ML services into customer environments.\nConfigure inference pipelines across cloud, on-prem, and hybrid infrastructure.\nPackage and deploy ML services via Docker/Kubernetes.\nEnsure inference services are reliable, scalable, and production-ready.\n2) Time Series & Predictive Model Tuning\nDeploy and tune time-series forecasting and anomaly detection models.\nAdapt models to customer-specific industrial processes.\nConfigure thresholds, alerting logic, and detection sensitivity.\nValidate model outputs against engineering expectations.\nTypical model classes include:\nGradient boosting models (LightGBM)\nTransformer models\nStatistical anomaly detection methods\nMultivariate monitoring systems\n3) Multi-Agent & LLM System Configuration\nDeploy and configure multi-agent AI systems for customer workflows.\nSet up LLM provider integrations (OpenAI, Claude, Gemini).\nConfigure agent routing and orchestration logic.\nTune prompts and workflows for operational use cases.\n4) Retrieval Augmented Generation (RAG)\nDeploy RAG pipelines in customer environments.\nIngest customer documentation and operational knowledge.\nConfigure knowledge graphs and vector databases.\nTune retrieval pipelines for accuracy and latency.\n5) Intelligent Data Agents\nConfigure SQL agents for structured customer datasets.\nDeploy visualization agents for exploratory analytics.\nAdapt agents to customer schemas and naming conventions.\n6) Explainability & Interpretability\nGenerate SHAP explanations for model outputs.\nBuild interpretability reports for engineering stakeholders.\nExplain anomaly drivers and optimisation recommendations.\nSupport trust and adoption of AI insights.\n7) Forward Deployment & Customer Integration\nDeploy AI systems into restricted industrial networks.\nIntegrate inference pipelines with:\no Historians\no OPC UA servers\no IoT data streams\no Process control systems\nWork with IT/OT teams to satisfy infrastructure and security constraints.\nDebug production issues in live operational environments.\n8) Production Reliability & MLOps\nMonitor inference performance and drift.\nTroubleshoot production model failures.\nVersion models and datasets (DVC or equivalent).\nMaintain containerised ML deployments.\nSupport CI/CD for model updates.","description_format":"text","description_chars":5436,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Chemical Manufacturing","Petroleum & Mining Engineering"],"lifecycle":[{"event":"open","at":"2026-10-09T15:33:49Z"}],"visa":[],"liveness":{"score":5,"band":"cold","label":"Long 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