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Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together. The Manager, AI/ML Engineering will lead the strategy, architecture, and delivery of enterprise-scale AI/ML, Generative AI, LLM, Agentic AI, and Intelligent Automation solutions that drive business value and operational excellence. The role is responsible for building and mentoring high-performing teams while delivering scalable, secure, and cloud-native AI platforms using Azure, Databricks, PySpark, Scala, Kubernetes, and MLOps frameworks. The manager will partner with Data Science, Product, Architecture, Engineering, and Business stakeholders to translate strategic priorities into AI solutions, ensuring engineering excellence, platform reliability, AI governance, and successful delivery of enterprise AI initiatives. The role will also drive adoption of emerging technologies such as LLMs, RAG, Multi-Agent Systems, Vector Search, and Responsible AI practices, while influencing technology roadmaps and innovation across the organization. Primary Responsibilities: Evaluate emerging AI technologies, frameworks, and tools to drive innovation and continuous improvementLead and manage a team of AI/ML Engineers responsible for building and supporting enterprise AI solutionsDefine and execute the organization's AI/ML and Generative AI strategy, aligned with business objectives and technology roadmapsDrive architecture and implementation of LLM, RAG, Agentic AI, Predictive Analytics, and Intelligent Automation solutionsEstablish engineering standards, development frameworks, AI governance controls, and operational best practicesOversee end-to-end AI solution lifecycle including design, development, testing, deployment, monitoring, and optimizationPartner with business leaders to identify opportunities for AI-driven transformation and measurable business impactLead architecture reviews, code reviews, technical design sessions, and technology evaluationsCreate technical documentation and provide support for AI/ML application deployments, monitoring, and incident resolutionDrive adoption of MLOps, CI/CD, Model Monitoring, Drift Management, and Responsible AI practicesManage project execution, delivery commitments, capacity planning, vendor engagement, and stakeholder communicationsEnsure enterprise AI platforms meet security, scalability, reliability, compliance, and operational requirementsBuilder Responsibilities:Design, develop, and deploy AI-powered solutions using no-code, low-code, and advanced platforms, translating business needs into scalable applications that enhance products, workflows, and decision-makingComply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do soRequired Qualifications: Bachelor's or Master's degree in Computer Science, Information Technology, or equivalent12+ years of professional experience in AI/ML engineering, including machine learning, Generative AI, LLM-based applications, Agentic AI frameworks, and Big data platform developmentSolid hands-on experience with Python and ScalaHands-on experience with Snowflake and solid expertise in SQL and PL/SQLHands-on experience with Docker, Kubernetes, and modern DevOps practicesExperience building large-scale batch and streaming data processing systemsExperience working in cloud environments, preferably Microsoft AzureExperience with Shell scripting for automation and operational supportExperience developing, training, fine-tuning, and deploying AI/ML models using frameworks such as scikit-learn, TensorFlow, PyTorch, and Generative AI/LLM ecosystemsExperience building and managing CI/CD pipelines using Jenkins, GitHub Actions, and Git-based workflowsExperience working in Agile development environmentsHands-on exposure to LLMs (e.g., OpenAI GPT, Azure OpenAI), including prompt design, fine-tuning concepts, and secure workflow integrationExpertise on REST APIs and FAST APIExpertise in Apache Spark and solid understanding of .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.