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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. We are looking for a Lead AI/ML Engineer to code and develop software that deploys ML models and algorithms into production. This role communicates and presents complex analytics results and concepts to leadership and internal stakeholders. The role employs AI and/or ML techniques that may include natural language processing (NLP), natural language understanding (NLU), semantic understanding, intent classification, computer vision, deep learning and automatic speech recognition (ASR). The engineer applies deep learning technologies to enable computers to visualize, learn, and respond to complex situations, and may adapt machine learning to areas such as artificial intelligence, robotics, and interactive user experiences. The role works with large-scale computing frameworks and data analysis systems. The role is expected to apply knowledge of experimental methodologies, statistics, optimization, probability theory, and machine learning using code for tool building and statistical analysis, using both general purpose software and statistical languages. Primary Responsibilities: Develop and implement AI and machine learning strategies across several healthcare domains Manage and lead the AI and engineering team, responsible for creating innovative solutions and delivering end-to-end machine learning projects with a high degree of value, ambiguity, scale, and complexity Design, develop, implement and/or optimize AI/ML, Generative AI, and Agentic solutions using cloud services (preferably Azure) Provide architectural guidance and define evaluation frameworks to ensure AI systems meet enterprise security, compliance and operational standards in complex healthcare data ecosystems Possess deep expertise in modern software engineering practices including AI/ML/Generative AI, Agile methodologies, and DevSecOps to deliver daily product deployments using full automation from code check-in to production across the full SDLC lifecycle Establish patterns, standards and reusable components for RAG, multi-agent systems, LLMOps and high performance inference Serve as a role model by leveraging these techniques to optimize solutioning and product delivery Demonstrate solid understanding of the full lifecycle product development, focusing on continuous improvement and learning Implement and oversee DevOps practices and CI/CD pipelines using GitHub Actions Ensure data security and compliance with industry standards Provide technical leadership and mentorship to the engineering team Ensure robustness, scalability and model performance aligned with business KPIs Work cross functionally with product, engineering, consulting, cloud, SMEs and clinical teams Comply 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 so Builder 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-making. Required Qualifications: Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, Statistics, or a related discipline 8+ years of experience in Software Engineering, Data Science, or Analytics, with at least 8 years in AI/ML engineering or related fields and the last 2 years focused on Generative AI technologies including OpenAI, Claude, Gemini, LangChain, Agents, RAG, Vector Databases, Prompt Engineering, and fine-tuning Experience with AI/ML frameworks and tools such as MLFlow, TensorFlow, PyTorch, Keras, and Scikit-learn Experience with cloud platforms such as AWS, Azure and GCP Experience with DevOps and CI/CD tools and practices including Git, Jenkins, Docker, and Kubernetes Demonstrated experience leading and managing AI/ML teams and projects from ideation to .
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.