{"id":3751905,"url":"https://alion.io/job/sedona-digital-mlops-engineer","title":"MLOps Engineer","company":{"id":689381,"name":"Sedona Digital","domain":"sedona.digital","url":"https://alion.io/company/sedona-2","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":95,"open_postings":4,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":112,"computed_at":"2026-11-07T05:47:08Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["RO","RS"],"hiring_countries_total":2,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon EC2","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Argo Workflows","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"Bash","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GitLab CI","optional":false},{"name":"Grafana","optional":false},{"name":"Helm","optional":false},{"name":"Keras","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"TensorFlow","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-11-04T15:35:00Z","employer_posted_date":"2026-11-04","last_verified_at":"2026-11-07T11:51:08Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Join Sedona Digital, a fast-growing scale-up organization with an ambition to be recognized as one of the leading technology companies servicing high-tech, global enterprises across Technology, Finance, and Life Sciences sectors. Our global client base needs builders: engineers and developers who love technology, have deep expertise in software, engineering, and cloud technologies, and importantly, have a passion for culture and customers.\nWe obsess about our customers \nWe build outstanding technical solutions\nWe create an awesome culture \nWe accelerate learning and careers \nWe are looking for a skilled MLOps Engineer to join our team and play a key role in bridging the gap between our Data Science and Infrastructure teams. You will be responsible for supporting the Data Science team in MLOps-related tasks while also helping in DevOps initiatives, including CI/CD pipeline creation, provisioning of cloud resources using tools like Terraform, and Kubernetes orchestration. You will collaborate closely with data scientists and engineers to deploy data pipelines, train machine learning models, and manage their deployment within scalable cloud environments while ensuring high performance, security, and reliability throughout the ML lifecycle.\nThe main responsibilities of the position include:\nAssist in designing, implementing, and maintaining scalable MLOps pipelines on AWS using services such as SageMaker, EC2, EKS, S3, Lambda and other relevant AWS tools \nCoordinate with our platform team to troubleshoot Kubernetes clusters (EKS) to orchestrate the deployment of machine learning models and other microservices \nDevelop and maintain CI/CD pipelines for model and application deployment, testing, and monitoring \nCollaborate closely with Data Science, and DevOps team to streamline the model development lifecycle, from experimentation to production deployment \nImplement security best practices, including network security, data encryption, and role-based access controls within the AWS infrastructure \nMonitor, troubleshoot, and optimize data and ML pipelines to ensure high availability and performance \nSet up and manage model monitoring systems for performance drift, ensuring continuous model improvement\nRequirements\nBachelor’s degree in Computer Science, Engineering, or related field \n2+ years of hands-on experience in MLOps, DevOps, or related fields \nKnowledge and preferable working experience in AWS services for machine learning, such as SageMaker, EKS, S3, EC2, Lambda, and others \nExposure to Kubernetes for container orchestration \nExperience with Docker Exposure to infrastructure-as-code tools such as Terraform or CloudFormation \nFamiliarity with CI/CD tools such as GitLab CI \nUnderstanding machine learning model lifecycle \nFamiliarity with monitoring and logging solutions like Prometheus, Grafana, CloudWatch and ELK Stack \nUnderstanding of networking concepts and cloud security best practices \nProficiency in Python and Bash, and comfortable working in Linux environments \nStrong problem-solving and communication skills\nThe following will be considered an advantage:\nExperience working with serverless architectures and event-driven processing on AWS \nFamiliarity with advanced Kubernetes concepts such as Helm \nExperience with Data Engineering pipelines, ETL processes, or big data platforms \nExperience with ML frameworks like TensorFlow, PyTorch and Keras \nExperience with ML platforms like Kubeflow and/or SageMaker \nExperience with workflow engines like Argo Workflows and/or Airflow\nBenefits\nWork from home \nMedical insurance given after the probation period \nTraining on market trends and client needs \nContinuous learning and personal development opportunities \nHoliday allowance in line with local legislation \nCompetitive compensation package \nPerformance-based bonus \nFun budget for team 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