{"id":825185,"url":"https://alion.io/job/factored-senior-mlops-engineer","title":"Senior MLOps Engineer","company":{"id":673411,"name":"Factored","domain":"factored.com","url":"https://alion.io/company/factored","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"D","score":52,"open_postings":10,"ghost_share":0.8,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":30,"computed_at":"2026-11-03T05:54:40Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["AR","BR","CO","MX","AI","AG","AW","BS","BB","BZ","BM","BO","VG","BQ","KY","CL","CR","CW","DM","DO","EC","SV","FK","GF","GD","GP","GT","GY","HT","HN","JM","MQ","MS","NI","PA","PY","PE","PR","KN","LC"],"hiring_countries_total":48,"salary":null,"salary_estimate":{"min_usd":55000,"max_usd":144000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2365},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon EKS","optional":false},{"name":"Arize Phoenix","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure AKS","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"DVC","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitLab CI","optional":false},{"name":"Google GKE","optional":false},{"name":"Grafana","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2025-03-26T15:50:45Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-11-04T03:01:35Z","board_verified":true,"closed_at":null,"days_open":587,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":586},"description":"Fully remote | Complete engagement job\nFounded in Palo Alto by Dr. Andrew Ng and Israel Niezen, Factored helps U.S. companies build and scale world-class AI, ML, and Data teams, powered by the top 1% of LATAM talent, with a defining purpose: To empower brilliant humans, unleash their potential, and amplify their impact in the world.\nAt Factored, you’ll be part of a community that values learning, ownership, and authenticity, where your growth is personal and your ideas matter. We’re transparent, curious, and collaborative. We strive for excellence, celebrate diversity, encourage curiosity, and build an environment where you can truly thrive.\nAs an MLOps Engineer, you will design, build, and maintain the infrastructure, pipelines, and tools that enable seamless machine learning deployment and operation across enterprise environments. You’ll bridge the gap between Machine Learning and Software Engineering, owning ML workflows end-to-end from automated training and versioning to production monitoring and continuous integration.\nFunctional Responsibilities:\nBuild and automate continuous integration and deployment (CI/CD) pipelines tailored for machine learning models and data workflows using tools like GitHub Actions, GitLab CI, or Jenkins.\nArchitect and manage containerized microservices and orchestration platforms for ML workloads using Docker and Kubernetes (EKS, GKE, AKS).\nImplement and manage ML lifecycle, tracking, and versioning tools such as MLflow, DVC, or Kubeflow.\nProvision, configure, and maintain ML cloud infrastructure using Infrastructure as Code (IaC) tools like Terraform.\nEstablish comprehensive monitoring, alerting, and logging systems for deployed models using tools like Prometheus, Grafana, or Arize to track data drift, model performance, and operational health.\nCollaborate with data scientists and software engineers to transition ML models from experimental scripts into secure, low-latency, and scalable production APIs.\nQualifications:\n5+ years of hands-on experience in MLOps, with a proven track record of deploying and scaling ML pipelines in enterprise environments.\nAdvanced English proficiency (written and spoken) with strong communication skills to collaborate effectively across multidisciplinary teams.\nStrong Python and Bash scripting skills, with expertise in containerization (Docker) and orchestration frameworks (Kubernetes).\nDeep hands-on experience with cloud platforms (AWS, GCP, Azure), Infrastructure as Code (Terraform), and CI/CD pipelines.\nDemonstrated experience with dedicated MLOps platforms (MLflow, DVC, Kubeflow) and model observability/monitoring solutions (Prometheus, Grafana, Arize).\nOur Benefits:\nOwnership through equity participation.\nAnnual company retreat.\nEducation bonus for continuous learning.\nCompany-wide winter break.\nPaid time off.\nOptional in-person events and meetups.\nTailored career roadmaps.\nHigh-performance culture.\nAt Factored, we believe that passionate, smart people expect honesty and transparency, as well as the freedom to do the best work of their lives while learning and growing as much as possible. Great people enjoy working with other passionate, smart people, so we believe in hiring right, and are very selective about who joins our team. Once we hire you, we will invest in you and support your career and professional growth in many meaningful ways. We hire people who are supremely intelligent and talented, but we recognize that intelligence is not enough. Perhaps more importantly, we look for those who are also passionate about our mission and are honest, diligent, collaborative, kind to others, and fun to be around. Life is too short to work with people who don’t inspire you. \nWe are a transparent workplace, where EVERYBODY has a voice in building OUR company, and where learning and growth are available to everyone based on their merits, not just on stamps on their resume. As impressive as some of the stamps on our resumes are, we recognize that human talent and passion exist everywhere, and come from many backgrounds, so stamps matter much less than results. All of us are dedicated doers and are highly energetic, focusing vehemently on execution because we know that the best learning happens by doing. We recognize that we are creating OUR COMPANY TOGETHER, which is not only a high-performing fast-growing business but is changing the way the world perceives the quality of technical talent in Latin America. We are fueled by the great positive impact we are making in the places where we do business and are committed to accelerating careers and investing in hundreds (and hopefully thousands) of highly talented data science engineers and data analysts. \nIn short, our business is about people, so we hire the best people and invest as much as possible in making them fall in love with their work, their learning, and their mission. When not nerding out on data science, we love to make music together, play sports, play games, dance salsa, cook delicious food, brew the best coffee, throw the best parties, and generally have a great time with each other.","description_format":"text","description_chars":5099,"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":[{"language":"English","level":"Advanced (C1)","optional":false}]},"benefits":["Continuous learning","Equity"],"hiring_locations":[{"name":"Argentina","iso":"AR","kind":"region"},{"name":"Brazil","iso":"BR","kind":"region"},{"name":"Colombia","iso":"CO","kind":"region"},{"name":"Mexico","iso":"MX","kind":"region"},{"name":"Anguilla","iso":"AI","kind":"region"},{"name":"Antigua and 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