{"id":2043910,"url":"https://alion.io/job/fractal-mlops-engineer-2","title":"MLOps Engineer","company":{"id":226,"name":"Fractal","domain":"fractal.ai","url":"https://alion.io/company/fractal-analytics","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":76,"open_postings":37,"ghost_share":0,"stale_share":0.946,"repost_share":0,"time_to_fill_p50_days":29,"computed_at":"2026-10-12T06:55:09Z"}},"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":["New York, United States","United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":100000,"max":125000,"currency":"USD","period":"year","gross":null,"usd_annual":125000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Aurora","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Copilot","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"FastAPI","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Platform Engineering","optional":false},{"name":"PostgreSQL","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"LLM","optional":true}],"status":"live","first_seen_at":"2026-10-07T18:55:42Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-12T13:04:30Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"It's fun to work in a company where people truly BELIEVE in what they are doing!\nWe're committed to bringing passion and customer focus to the business.\nFractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets; an ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner.\nRole Summary\nMLOps Engineer - Consultant\nWe are hiring a senior MLOps Engineer on a consulting basis to help operationalize a portfolio of machine learning solutions in purchase and underwriting. You will work alongside our AI/MLOps team and partner with our Data Science and Data Engineering teams to deliver inference services, data pipelines, feature engineering frameworks, and model lifecycle management capabilities.\nThis is a hands-on engineering role. You should expect to spend most of your time writing production code, designing scalable services, building reusable ML platform capabilities, and ensuring machine learning solutions are production-ready across development, test, and production environments.\nScope of Work\nModel Serving - Synchronous and Asynchronous\nDesign and build FastAPI services that expose models to downstream applications, including request/response contracts, authentication and authorization, input validation, error handling, structured logging, tracing, and metrics. \nImplement queue-based asynchronous serving patterns for higher-latency and higher-throughput workloads using technologies such as SQS and Kafka. \nContainerize services with Docker and deploy them onto Kubernetes/EKS environments with production-grade observability and scalability. \nDesign and implement model inference patterns across both batch and real-time serving use cases. \nML Platform and Lifecycle Management\nDesign and implement end-to-end MLflow-based patterns for model training, experiment tracking, model registry, deployment, and inference. \nBuild and manage reproducible ML workflows, ensuring seamless model promotion across development, testing, and production environments. \nManage model artifacts, versions, lineage, and deployment governance using MLflow and Databricks-native capabilities. \nEstablish standardized MLOps frameworks and reusable implementation patterns that can be adopted across multiple ML use cases. \nData Engineering and Feature Pipelines\nBuild reproducible training and inference pipelines on Databricks and PySpark, from raw sources through curated feature and training datasets. \nDesign and develop pipelines leveraging Databricks capabilities including MLflow, Delta Lake, Databricks Workflows, and Databricks Asset Bundles (DABs). \nOwn data preprocessing, transformation, and feature engineering code and evolve existing solutions into scalable, reusable production frameworks. \nEnsure consistent feature engineering and data processing logic across training, batch inference, and real-time serving paths. \nSolution Architecture and Design\nParticipate in solution design discussions with Data Science, Data Engineering, Platform Engineering, business, and IT stakeholders. \nCreate technical architecture diagrams, solution blueprints, design documents, and implementation runbooks. \nEvaluate tradeoffs between scalability, maintainability, performance, and operational complexity while defining target-state architectures. \nProvide technical guidance and recommendations on MLOps best practices, deployment approaches, and platform adoption. \nReliability and Observability\nImplement monitoring for model performance, prediction drift, data quality, service health, and pipeline reliability. \nDiagnose production incidents across pipelines and services, identify root causes, and drive durable fixes. \nEstablish operational standards for deployment, monitoring, alerting, and supportability. \nEngineering Practices\nApply strong software engineering fundamentals including testing, code reviews, CI/CD, versioning, dependency management, and infrastructure automation. \nBuild and maintain shared libraries, starter templates, reusable frameworks, and engineering standards. \nLeverage GitHub Copilot and Claude Code to improve developer productivity, code quality, and delivery velocity. \nDocument solutions clearly enough for internal teams to own and operate after the engagement concludes. \nWhat You Bring\nRequired\nDeep hands-on Python expertise for both data engineering and backend application development. \nStrong experience developing production-grade backend services using FastAPI. \nHands-on expertise with the Databricks platform, including: \nMLflow \nDelta Lake \nDatabricks Workflows \nDatabricks Asset Bundles (DABs) \nSpark-based distributed processing \nProven experience designing and implementing end-to-end MLflow-based training and inference architectures across multiple environments. \nExperience with AWS services including SQS, EKS, and Aurora PostgreSQL. \nExperience implementing event-driven and asynchronous architectures using Kafka and/or SQS. \nStrong understanding of the end-to-end ML lifecycle, including feature engineering, training, deployment, monitoring, and retraining. \nExperience creating architecture diagrams, technical design documentation, implementation plans, and operational runbooks. \nStrong Docker and Kubernetes fundamentals. \nExperience with CI/CD pipelines using GitHub Actions, Jenkins, or similar tools. \nHands-on experience using both GitHub Copilot and Claude Code as part of day-to-day software engineering workflows. \nExcellent written and verbal communication skills with the ability to collaborate effectively with technical and business stakeholders. \nNice to Have:\nPrior experience working in Group Insurance, Life Insurance, or Underwriting domains. \nExperience operationalizing GenAI or LLM-based applications and services. \nPay:\nThe wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Fractal, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is: $100,000 - $125,000. In addition, you may be eligible for a discretionary bonus for the current performance period.\nBenefits:\n As a full-time employee of the company or as an hourly employee working more than 30 hours per week, you will be eligible to participate in the health, dental, vision, life insurance, and disability plans in accordance with the plan documents, which may be amended from time to time. You will be eligible for benefits on the first day of Employment with the Company. In addition, you are eligible to participate in the Company 401(k) Plan after 30 days of employment, in accordance with the applicable plan terms. The Company provides 11 paid holidays and 12 weeks of Parental Leave. We also follow a “free time” PTO policy, allowing you the flexibility to take the time needed for either sick time or vacation.\nFractal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.\nIf you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!\nNot the right fit? Let us know you're interested in a future opportunity by clicking Introduce Yourself in the top-right corner of the page or create an account to set up email alerts as new job postings become available that meet your interest!","description_format":"text","description_chars":8501,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":["Life insurance","Parental leave"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Machine Learning","Decision Intelligence","AI Consulting & Integration","Analytics & BI Consulting"],"lifecycle":[{"event":"open","at":"2026-10-07T18:55:42Z"}],"visa":[],"liveness":{"score":61,"band":"ok","label":"Likely open","p_open":1,"p_active":0.614,"p_room":1,"age_days":4,"expected_fill_days":29,"reasons":["conf:1","stale_co","velocity","win:early"],"computed_at":"2026-10-12T06:55:09Z"},"pay":{"stated_usd_annual":125000,"is_top_pay":false},"html_url":"https://alion.io/job/fractal-mlops-engineer-2","json_url":"https://alion.io/job/fractal-mlops-engineer-2.json","meta":{"generated_at":"2026-10-12T13:31:04Z","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 with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":21203,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-13T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":226},"rest":"https://alion.io/mcp/rest/get_company?id=226"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Ffractal-mlops-engineer-2"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Ffractal-mlops-engineer-2"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Ffractal-mlops-engineer-2"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/fractal-mlops-engineer-2\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Ffractal-mlops-engineer-2"}]}