{"id":755996,"url":"https://alion.io/job/coderoad-machine-learning-operations-engineer-mlops","title":"Machine Learning Operations Engineer (MLOps)","company":{"id":673115,"name":"CodeRoad","domain":"coderoad.com","url":"https://alion.io/company/coderoad","size_band":null,"is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"C","score":69,"open_postings":23,"ghost_share":0.522,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":43,"computed_at":"2026-10-05T05:45:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"contractor","work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["AR","BR","MX","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","PM","SX"],"hiring_countries_total":45,"salary":null,"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AgentOps","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"BigQuery","optional":false},{"name":"CI/CD","optional":false},{"name":"GCP","optional":false},{"name":"GDPR","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Grafana","optional":false},{"name":"IAM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Prometheus","optional":false},{"name":"Vertex AI","optional":false},{"name":"CrewAI","optional":true},{"name":"Docker","optional":true},{"name":"Git","optional":true},{"name":"Kubernetes","optional":true},{"name":"n8n","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-01-26T00:58:51Z","employer_posted_date":"2026-06-15","last_verified_at":"2026-10-06T00:21:01Z","board_verified":true,"closed_at":null,"days_open":253,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":252},"description":"Machine Learning Operations (MLOps) Engineer\nThe Team\nAt Coderoad, we're more than just a software development company-we're your gateway to the global tech world. Whether you're looking to skill up or level up your career, we offer the challenges you’ve been searching for.\nWe provide end-to-end software development services and give you the opportunity to work on exciting, real-world projects in a supportive environment. Whether it's staff augmentation, dedicated IT teams, or general software engineering, we have opportunities for everyone to challenge themselves and take their career to the next level!\nPosition Location - Latam (Remote).\nTime Zone Requirements - This team operates on the East/West Coast time zones.\nAbout the Role\nWe are seeking a skilled and innovative Machine Learning Operations (MLOps) Engineer with a focus on Agentic AI to design, deploy, and maintain scalable, robust, and ethical autonomous AI systems. The ideal candidate will combine deep expertise in modern MLOps practices with a solid understanding of agentic AI principles, enabling the seamless integration, monitoring, and optimization of AI models that exhibit autonomous decision-making and adaptability. You will be a key contributor in our cross-functional teams, ensuring our agentic AI solutions are reliable, efficient, and aligned with our business goals and ethical standards.\nKey Responsibilities\nModel Deployment & Integration: Design and implement scalable, secure, and production-grade pipelines for deploying agentic AI models. Focus on seamless integration with existing systems and enable real-time adaptability for autonomous decision-making.\n\nCloud Infrastructure Management: Build and maintain robust cloud infrastructure on Google Cloud Platform (GCP) or Amazon Web Services (AWS) for the entire AI lifecycle. Leverage services like GCP's Vertex AI and Cloud Functions, or their AWS equivalents such as Amazon SageMaker, and AWS Lambda, to create efficient and resilient environments.\n\nAutomation & CI/CD: Develop and maintain automated workflows for continuous integration, continuous deployment (CI/CD), and continuous training (CT) of agentic AI models. Optimize for performance, scalability, and reliability using CI/CD platforms.\n\nMonitoring & Performance Optimization: Implement and manage advanced monitoring systems to track the performance, health, and decision-making accuracy of agentic AI models in production. Utilize specialized tools like Lantrace, AgentOps, or AWS's CloudWatch to detect and resolve issues related to model drift, latency, and bias in real-time.\n\nSecurity & Compliance: Integrate security best practices throughout the MLOps lifecycle. Ensure agentic AI systems adhere to ethical guidelines and regulatory requirements, implementing safeguards for data privacy, bias mitigation, and transparency in autonomous operations.\n\nCollaboration: Work closely with AI researchers, data scientists, software engineers, and product teams to align MLOps processes with project goals. Facilitate iterative development and deployment of agentic AI solutions.\n\nData & Model Governance: Establish and enforce robust data and model governance frameworks, ensuring data quality, security, and compliance with industry standards for all agentic AI systems.\n\nQualifications\nExperience: 4+ years of experience in MLOps, DevOps, or a related field, with at least 1 year focused on deploying and managing AI/ML models in production. Experience with agentic or autonomous AI systems is highly preferred.\n\nCloud Expertise: (4years)Deep hands-on experience with either Google Cloud Platform (GCP) or Amazon Web Services (AWS). Knowledge of relevant services such as GCP's Vertex AI, Cloud Storage, BigQuery, and Cloud Functions or AWS equivalents like Amazon SageMaker, S3, Redshift, and Lambda.\n\nTechnical Stack: (1 year or less)Strong knowledge of MLOps tools and frameworks(Pytorch, Langraph, CrewAI, N8N). Proficiency in containerization with Docker and orchestration with Kubernetes.\n\nProgramming & Scripting: Expertise in Python and familiarity with scripting for automation (e.g., Bash, Terraform). Strong experience with version control systems, particularly Git.\n\nMonitoring & Analytics: Hands-on experience with modern monitoring tools like Lantrace, AgentOps, Prometheus, or AWS's CloudWatch and Grafana. Proven ability to track model performance, data drift, and system health in a production environment.\n\nSecurity Mindset: A strong understanding of security principles related to cloud and MLOps, including Identity and Access Management (IAM), data encryption, and secure pipeline design.\n\nEthical AI Knowledge: Understanding of ethical AI principles, including bias detection, explainability, and compliance with regulations like GDPR or other relevant standards.\n\nCollaboration & Communication: Strong interpersonal and communication skills, with the ability to work effectively in cross-functional teams and explain technical concepts clearly to diverse stakeholders.\n\nEducation: Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field. 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