{"id":505583,"url":"https://alion.io/job/oversight-machine-learning-engineer-llms-and-agentic","title":"Machine Learning Engineer - LLMs and Agentic","company":{"id":178057,"name":"Oversight","domain":"oversight.com","url":"https://alion.io/company/oversight-2","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"ADP","truth_index":{"grade":"B","score":75,"open_postings":5,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-24T06:02:35Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Atlanta, United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":109000,"max_usd":215000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":85},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"Claude","optional":false},{"name":"Databricks","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Gemini","optional":false},{"name":"GPT-4","optional":false},{"name":"Hugging Face","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Multimodal AI","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Ray","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Transformers","optional":false},{"name":"AutoGen","optional":true},{"name":"Azure","optional":true},{"name":"CI/CD","optional":true},{"name":"GCP","optional":true},{"name":"LLM Guardrails","optional":true}],"status":"live","first_seen_at":"2025-10-20T20:22:00Z","employer_posted_date":"2025-10-20","last_verified_at":"2026-10-24T14:08:10Z","board_verified":true,"closed_at":null,"days_open":368,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":368},"description":"About Oversight\nOversight is the world’s leading provider of AI-based spend management and risk mitigation solutions for large enterprises. Based in Atlanta, GA, Oversight works with many of the world’s most innovative companies and government agencies to digitally transform their spend audit and financial control processes.\nOversight’s AI-powered platform works across our customers’ financial systems to continuously monitor and analyze all spend transactions for fraud, waste, and misuse. With a consolidated, consistent view of risk across their enterprise, customers can prevent financial loss and optimize spend while strengthening the controls that improve compliance. Learn More.\nPositionOverview:\nWe are seeking a skilled and forward-looking ML Engineer with experience in Large Language Models (LLMs), generative AI, and agentic architectures to join our growing R&D and Applied AI team. This role is critical in helping Oversight deliver the next generation of agentic AI systems for enterprise spend management and risk controls.\nThe ideal candidate has a strong foundation in machine learning, modern deep learning frameworks, and data pipelines, coupled with hands-on experience experimenting with LLMs, small language models (SLMs), multi-agent frameworks, and retrieval-augmented generation (RAG).\nYou will work closely with AI/ML researchers, data engineers, and product teams to design, implement, and optimize models that power autonomous exception resolution, anomaly detection, and explainable insights. This is a hands-on engineering role where you will not only build and scale ML systems but also actively contribute to cutting-edge applied research in agentic AI.\nCore ML/LLM Engineering\nContribute to the design, training, fine-tuning, and deployment of ML/LLM models for production.\nImplement RAG pipelines using vector databases.\nWork with frameworks like LangChain, LangGraph, MCP to prototype and optimize multi-agent workflows.\nDevelop prompt engineering, optimization, and safety techniques for agentic LLM interactions.\nIntegrate memory, evidence packs, and explainability modules into agentic pipelines.\nWork hands-on with multiple LLM ecosystems:OpenAI GPT models (GPT-4, GPT-4o, fine-tuned GPTs).\nAnthropic Claude (Claude 2/3 for reasoning and safety-aligned workflows).\nGoogle Gemini (multimodal reasoning, advanced RAG integration).\nMeta LLaMA (fine-tuned/custom models for domain-specific tasks).\n\nData & Infrastructure\nCollaborate with Data Engineering to build and maintain real-time and batch data pipelines that serve ML/LLM workloads.\nConduct feature engineering, preprocessing, and embeddings generation for structured and unstructured data.\nImplement model monitoring, drift detection, and retraining pipelines.\nLeverage cloud ML platforms (AWS Sagemaker, Databricks ML) for experimentation and scaling.\nResearch & Applied Innovation\nExplore and evaluate emerging LLM/SLM architectures and agent orchestration patterns.\nExperiment with generative AI and multimodal models to extend capabilities beyond text (images, structured financial data).\nCollaborate with R&D to prototype autonomous resolution agents, anomaly detection models, and reasoning engines.\nTranslate research prototypes into production-ready components.\nCollaboration & Delivery\nWork cross-functionally with R&D, Data Science, Product, and Engineering to deliver business-aligned AI features.\nParticipate in design reviews, architecture discussions, and model evaluations.\nDocument processes, experiments, and results effectively for knowledge sharing.\nMentor junior engineers and contribute to ML engineering best practices.\nEducation,ExperienceandSkills\nRequired\nBachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related field.\n3+ years of experience building and deploying ML systems.\nProficiency in Python and libraries such as PyTorch, TensorFlow, Scikit-Learn, Hugging Face Transformers.\nHands-on experience with LLMs/SLMs (fine-tuning, prompt design, inference optimization).\nDemonstrated experience with at least two of the following ecosystems:OpenAI GPT models (chat, assistants, fine-tuning).\nAnthropic Claude (safety-first AI for reasoning and summarization).\nGoogle Gemini (multimodal reasoning, enterprise-scale APIs).\nMeta LLaMA (open-source, fine-tuned models).\n\nFamiliarity with vector databases, embeddings, and RAG pipelines.\nAbility to work with structured and unstructured data at scale.\nKnowledge of SQL and distributed data frameworks (Spark, Ray).\nStrong understanding of ML lifecycle: data prep, training, evaluation, deployment, monitoring.\nPreferred Qualifications\nExperience with agentic frameworks (LangChain, LangGraph, MCP, AutoGen).\nKnowledge of AI safety, guardrails, and explainability techniques.\nHands-on experience deploying ML/LLM solutions in cloud environments (AWS, GCP, Azure).\nExperience with CI/CD for ML (MLOps), monitoring, and observability.\nFamiliarity with anomaly detection, fraud/risk modeling, or behavioral analytics.\nContributions to open-source AI/ML projects or publications in applied ML research.","description_format":"text","description_chars":5105,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Fraud Detection","Procurement & Spend Management"],"lifecycle":[{"event":"open","at":"2026-09-07T12:00:52Z"}],"visa":[],"liveness":{"score":5,"band":"cold","label":"Long shot","p_open":1,"p_active":0.162,"p_room":0.28,"age_days":368,"expected_fill_days":22,"reasons":["conf:1","win:tail","crowd:"],"computed_at":"2026-10-24T06:02:35Z"},"pay":null,"html_url":"https://alion.io/job/oversight-machine-learning-engineer-llms-and-agentic","json_url":"https://alion.io/job/oversight-machine-learning-engineer-llms-and-agentic.json","meta":{"generated_at":"2026-10-24T17:09:32Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work 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