{"id":3690780,"url":"https://alion.io/job/island-io-ml-ops-engineer","title":"ML Ops Engineer","company":{"id":40053,"name":"Island.io","domain":"island.io","url":"https://alion.io/company/island-io","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Tel Aviv, Israel"],"countries":["IL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"GitHub Actions","optional":false},{"name":"IAM","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"ONNX","optional":false},{"name":"Quantization","optional":false},{"name":"Terraform","optional":false},{"name":"TGI","optional":false},{"name":"Triton","optional":false},{"name":"Vertex AI","optional":false},{"name":"vLLM","optional":false}],"status":"live","first_seen_at":"2026-11-03T12:08:02Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-07T21:42:18Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"Island is shaping the future of work - creating an enterprise workspace for both knowledge workers and agents - where access, security, and AI-powered productivity are built in. Our enterprise agentic control plane boosts productivity across devices, browsers, applications, networks, and data while protecting sensitive information, simplifying access, and helping enterprises scale AI safely.\nBehind all of that is a team of people who care deeply about their craft and want to do the biggest and best work of their careers. At Island, character and drive matter more than your resume. \nBacked by Coatue Management, Insight Partners, Sequoia Capital, Cyberstarts, and other world-class investors, Island is used by millions of people at some of the planet’s largest enterprises.\nCome build with us. Together, we’re changing how the world works. \nAbout the Role\nIsland’s AI security features run on machine learning models. Some we train ourselves, and some are open-weight models from the community. As an ML Ops Engineer, you will own the path from research to production: packaging, release, serving, and keeping models fast, reliable and cost-efficient. You will work closely with our researchers and backend engineers.\nWhat You’ll Do\nBuild and run the infrastructure that serves ML models in production, on AWS SageMaker and other cloud platforms.\nOwn the model release flow: packaging, versioning, CI/CD, safe rollout and rollback.\nHost and run open-weight models and LLMs on CPU and GPU.\nBuild pipelines for datasets, training jobs and evaluation, together with Research.\nMonitor models in production (latency, errors, quality, drift) and set up alerts.\nKeep compute costs under control: choose the right instance sizes and track GPU spend.\nYou have taken ML models to production. You don’t need to train models, but you understand how they are built, evaluated and served.\nYou Might Be a Fit If\nYou have 4+ years in DevOps, platform or backend engineering, with real production ownership\nYou have taken ML models to production. You don’t need to train models, but you understand how they are built, evaluated and served.\nYou have strong cloud skills, AWS in particular (IAM, S3, networking).\nYou have experience with infrastructure as code (Terraform or similar).\nYou have built CI/CD pipelines (GitHub Actions or similar) and work with Docker.\nYou use AI coding tools in your daily work.\nYou are a team player who takes ownership and learns fast.\n\n We’d Love It If You Have\nHands-on experience with AWS SageMaker (endpoints, training jobs or pipelines).\nExperience with ML services on other clouds (Azure ML, Vertex AI or similar).\nExperience serving LLMs (vLLM, TGI, Triton or similar) and running GPU instances.\nExperience with model optimization (ONNX, quantization).\nExperience with Kubernetes.\nExperience with ML workflow tools (MLflow, SageMaker Pipelines, Airflow or similar).\nData Privacy\nYou can find information on how we process and protect your personal data in our Candidate and Employee Privacy Notice. 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