{"id":4173131,"url":"https://alion.io/job/the-cigna-group-senior-ml-ops-engineer-nas-neuron","title":"Senior ML Ops Engineer - Nas Neuron","company":{"id":13928,"name":"The Cigna Group","domain":"thecignagroup.com","url":"https://alion.io/company/thecignagroup","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":88,"open_postings":83,"ghost_share":0.048,"stale_share":0.494,"repost_share":0.12,"time_to_fill_p50_days":20,"computed_at":"2026-11-12T10:38:37Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Dubai, United Arab Emirates"],"countries":["AE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Ansible","optional":false},{"name":"Azure","optional":false},{"name":"Azure AKS","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Azure DevOps","optional":false},{"name":"Bicep","optional":false},{"name":"CI/CD","optional":false},{"name":"Configuration Management","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Git","optional":false},{"name":"GitHub","optional":false},{"name":"GitLab CI","optional":false},{"name":"Hallucination","optional":false},{"name":"Incident Management","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"Linux","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"Microsoft Entra ID","optional":false},{"name":"MLFlow","optional":false},{"name":"OpenAI","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-11-12T15:18:54Z","employer_posted_date":"2026-11-12","last_verified_at":"2026-11-12T15:18:54Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Job Description\nThe Senior MLOps Engineer is responsible for operationalising and maintaining machine learning, Generative AI, and agent-based solutions in production. The role focuses on ensuring that AI services are deployed through controlled pipelines, monitored continuously, tested systematically, and operated with the reliability, security, scalability, and cost discipline required for enterprise use.\nWorking under the Principal - ML Platform Engineer, the role provides the operational discipline required to move AI solutions from experimentation into stable production service. This includes managing CI/CD, versioning, observability, model and agent monitoring, automated testing, incident response, release controls, and performance reporting. The Senior MLOps Engineer is expected to identify failures or degradation early, strengthen production runbooks, and continuously improve the effectiveness and resilience of deployed AI systems. Alongside this specialism, every engineer in the Strategy and Data team is expected to own use cases end-to-end, taking them from problem definition through to a deployed, adopted solution.\nResponsibilities and Duties\nDesign, build, and maintain production-grade MLOps pipelines covering deployment, release management, monitoring, automated testing, versioning, rollback, retraining support, and decommissioning.\nEstablish and operate CI/CD pipelines for machine learning, Generative AI, and agent-based workloads using appropriate enterprise tooling such as Azure DevOps, GitHub, GitLab CI, Jenkins, or equivalent platforms.\nPackage, containerise, deploy, and maintain models, APIs, AI agents, RAG workflows, and inference services across controlled development, test, staging, and production environments.\nImplement automated testing for model services, prompts, agent workflows, APIs, data dependencies, deployment packages, and release gates to reduce production risk and improve service reliability.\nMonitor model, agent, and service performance, including latency, throughput, error rates, hallucination or response-quality signals, drift indicators, data quality issues, dependency failures, and user-impacting incidents.\nDevelop observability dashboards, alerts, logs, traces, and health checks that provide early warning of failures, degradation, cost anomalies, security risks, or operational instability.\nMaintain model and agent versioning, artifact repositories, configuration records, environment definitions, dependency controls, and release documentation to support reproducibility and auditability.\nSupport production incident management for AI services, including triage, root-cause analysis, remediation, escalation, post-incident review, and preventive improvement actions.\nPrepare clear performance and operational reporting covering service health, reliability, release quality, incident trends, utilisation, cost, and improvement actions for technical and management stakeholders.\nMonitor cloud and AI service consumption, identify cost drivers, recommend optimisation opportunities, and support capacity planning for production AI workloads.\nWork with AI engineers, data engineers, software engineers, IT, and security teams to ensure deployments follow approved architecture, security, privacy, responsible AI, and governance standards.\nSupport integration between Snowflake, Microsoft Azure, data pipelines, vector stores, model services, agent orchestration layers, monitoring tools, and downstream enterprise applications.\nMaintain operating procedures, runbooks, release checklists, escalation paths, environment standards, and reusable deployment templates to strengthen the organisation’s production AI capability.\nContinuously improve the stability, scalability, security, and effectiveness of production AI systems, ensuring that deployed solutions remain aligned with business priorities and operational needs.\nTake assigned use cases through each stage, from framing the problem with business stakeholders and preparing data, to building, evaluating and deploying the solution and tracking its adoption and value, working within the team's architecture and engineering standards.\nKnowledge, Skills and Experience\n8+ years of relevant experience across MLOps, DevOps, platform engineering, data engineering, software engineering, or machine learning engineering, with direct exposure to production AI, analytics, or data services.\nStrong practical experience with Python, SQL, Git/GitHub, MLflow, Azure AI Foundry, Snowflake/Snowpark, Docker, Kubernetes, FastAPI, Airflow, Azure Data Factory, Azure Data Lake, Azure Container Apps, Microsoft Azure, Entra ID, Key Vault, Private Link, Terraform, REST APIs, Linux environments, cloud deployment patterns, and production support practices.\nDemonstrated experience operating CI/CD pipelines, release controls, branching strategies, pull requests, automated testing, artifact management, environment promotion, and rollback processes.\nHands-on experience deploying and operating containerised AI services using Kubernetes, Azure Kubernetes Service, Azure App Service, Azure Functions, or equivalent platforms.\nPractical knowledge of Microsoft Azure services relevant to AI and MLOps, including Azure Machine Learning, Azure AI Foundry, Azure OpenAI, Azure Monitor, Application Insights, Log Analytics, Key Vault, Storage, and networking controls.\nExperience with Snowflake or equivalent cloud data platforms, including integration with model training, inference services, feature access, orchestration layers, and downstream applications.\nFamiliarity with MLOps and LLMOps practices such as MLflow, model registries, experiment tracking, model artifact management, prompt evaluation, agent monitoring, vector stores, embeddings, and RAG workflow operations.\nStrong understanding of observability, including metrics, logs, traces, alerts, service-level indicators, drift monitoring, model degradation, inference latency, throughput, error rates, and retraining triggers.\nExperience supporting Generative AI or agent-based solutions preferred, including prompt and response evaluation, orchestration monitoring, tool-use tracking, guardrail monitoring, and endpoint management.\nStrong understanding of secure engineering practices, including identity and access management, secrets management, private endpoints, vulnerability management, dependency control, secure API deployment, and environment isolation.\nKnowledge of responsible AI, data privacy, model governance, auditability, human oversight, and regulated-environment controls, preferably in healthcare, insurance, financial services, or another compliance-sensitive sector.\nExperience with infrastructure as code, automation scripting, and configuration management tools such as Terraform, Bicep, ARM templates, Ansible, or similar tools preferred.\nAbility to investigate incidents, identify root causes, assess operational risk, communicate technical findings clearly, and drive practical remediation actions.\nBachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, Information Systems, or a related discipline.\nRelevant certifications in cloud engineering, DevOps, MLOps, data engineering, cybersecurity, or machine learning preferred.\nStrong analytical, documentation, stakeholder management, and communication skills, with the ability to translate production performance, risks, and improvement priorities into clear management reporting.\nStatements in this Job Description are intended to reflect, in general, the duties and responsibilities of the position, but they are not to be interpreted as totally inclusive. NAS Neuron Heath Services LLC is an equal opportunity employer.\nAbout The Cigna Group\nDoing something meaningful starts with a simple decision, a commitment to changing lives. At The Cigna Group, we’re dedicated to improving the health and vitality of those we serve. Through our divisions Cigna Healthcare and Evernorth Health Services, we are committed to enhancing the lives of our clients, customers and patients. Join us in driving growth and improving lives.Qualified applicants will be considered without regard to race, color, age, disability, sex, childbirth (including pregnancy) or related medical conditions including but not limited to lactation, sexual orientation, gender identity or expression, veteran or military status, religion, national origin, ancestry, marital or familial status, genetic information, status with regard to public assistance, citizenship status or any other characteristic protected by applicable equal employment opportunity laws.\nIfyou require reasonable accommodation in completing the online application process, please email:  for support. Do not email  for an update on your application or to provide your resume as you will not receive a response.","description_format":"text","description_chars":8890,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United Arab Emirates","iso":"AE","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Pharmacies","Health Insurance & Benefits"],"lifecycle":[{"event":"open","at":"2026-11-12T15:18:54Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":20,"reasons":["conf:4","win:early","comp:brand"],"computed_at":"2026-11-12T19:24:27Z"},"pay":null,"html_url":"https://alion.io/job/the-cigna-group-senior-ml-ops-engineer-nas-neuron","json_url":"https://alion.io/job/the-cigna-group-senior-ml-ops-engineer-nas-neuron.json","meta":{"generated_at":"2026-11-12T19:24:27Z","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":40907,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-13T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.1,"usd":0.01,"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":13928},"rest":"https://alion.io/mcp/rest/get_company?id=13928"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fthe-cigna-group-senior-ml-ops-engineer-nas-neuron"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":2,"usd":0.2},"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%2Fthe-cigna-group-senior-ml-ops-engineer-nas-neuron"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.1,"usd":0.01},"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%2Fthe-cigna-group-senior-ml-ops-engineer-nas-neuron"},{"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/the-cigna-group-senior-ml-ops-engineer-nas-neuron\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fthe-cigna-group-senior-ml-ops-engineer-nas-neuron"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":4173131}},{"tool":"search_jobs","arguments":{"company":"thecignagroup.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":4173131},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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