The Role
Design, develop, deploy, and optimize production AI/ML solutions for healthcare. Build machine learning, deep learning, NLP, computer vision, generative AI, RAG, and agentic systems; integrate models with enterprise platforms; develop MLOps and LLMOps pipelines; implement monitoring, security, guardrails, evaluation, and human-in-the-loop controls; and communicate technical approaches and risks to stakeholders.
Summary Generated by Built In
Requisition Number: 2397574
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.
Primary Responsibilities:
Required Qualifications:
At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone - of every race, gender, sexuality, age, location and income - deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.
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.
Primary Responsibilities:
- Design, develop, and deploy scalable AI/ML solutions across healthcare domain
- Select, implement, and optimize supervised and unsupervised learning algorithms for classification, regression, clustering, anomaly detection, and forecasting
- Learn and adopt AIDLC as the standard development approach, and promote its adoption through training and knowledge sharing
- Develop deep learning, NLP, computer vision, recommender-system, reinforcement-learning, and generative AI models based on business needs
- Perform feature engineering, hyperparameter tuning, cross-validation, error analysis, and model comparison using appropriate evaluation metrics
- Build retrieval-augmented generation and Agentic AI solutions with planning, reasoning, memory, tool use, and multi-agent orchestration
- Integrate models and AI agents with enterprise APIs, applications, data sources, vector databases, and workflow platforms
- Implement human-in-the-loop controls, guardrails, access controls, and escalation paths for high-impact AI workflows
- Develop reliable data ingestion, training, inference, and model-output pipelines
- Evaluate models and agents for predictive performance, bias, retrieval quality, hallucination, safety, latency, reliability, and cost
- Design and maintain CI/CD and LLMOps/MLOps pipelines, including automated testing, versioning, deployment, monitoring, and rollback
- Develop secure, scalable services using Python, FastAPI, REST APIs, containers, and cloud or Kubernetes platforms
- Communicate AI/ML approaches, algorithm choices, trade-offs, risks, and results clearly to technical and non-technical stakeholders
- Comply with all applicable Company policies, procedures, and business directives, changes including those relating to work location, team assignments, work schedules, and flexible work arrangements
Required Qualifications:
- Graduate degree or equivalent experience
- Bachelor's degree or higher in Computer Science, Engineering, Data Science, Statistics, Mathematics, or a related field
- 4+ years of experience in software engineering, data science, analytics, or related fields, including 2+ years of hands-on AI/ML engineering experience
- Experience with deep learning architectures such as feed-forward networks, CNNs, RNNs/LSTMs, attention mechanisms, and Transformers
- Experience applying algorithms to NLP, computer vision, recommender systems, reinforcement learning, and generative AI use cases
- Experience developing and deploying NLP, large language model, retrieval-augmented generation, and generative AI solutions
- Hands-on experience designing Agentic AI solutions using frameworks such as LangGraph, LangChain, AutoGen, Semantic Kernel, or CrewAI
- Experience building agent workflows with planning, reasoning, tool and function calling, task delegation, memory, context management, and human-in-the-loop controls
- Experience with LLMOps/MLOps practices, including experiment tracking, dataset, prompt, and model versioning, automated testing, monitoring, deployment, and rollback
- Experience developing production services with FastAPI, Flask, REST APIs, Streamlit, Docker, and Kubernetes
- Experience deploying data pipelines, models, or AI applications on Azure, AWS, or GCP; Azure, Azure Machine Learning, and Databricks experience is preferred
- Solid understanding of supervised and unsupervised learning algorithms, including linear and logistic regression, decision trees, random forests, gradient boosting, support vector machines, nearest-neighbor methods, clustering, dimensionality reduction, anomaly detection, and time-series forecasting
- Knowledge of feature engineering, sampling, regularization, hyperparameter optimization, cross-validation, class-imbalance handling, explainability, and model-selection techniques
- Ability to select and interpret evaluation metrics such as precision, recall, F1 score, ROC-AUC, PR-AUC, log loss, RMSE, MAE, ranking metrics, and business-specific measures
- Solid proficiency in Python and PySpark, with hands-on experience using Scikit-learn, TensorFlow or PyTorch, Pandas, NumPy, and MLflow
- Familiarity with open-source and commercial LLMs, Hugging Face, model APIs, prompt engineering, embeddings, vector databases, and retrieval architectures
- Ability to evaluate and secure AI systems using observability, guardrails, responsible AI practices, hallucination mitigation, retrieval-quality assessment, and performance and cost optimization
- Proven excellent analytical and problem-solving skills, including root-cause analysis, algorithmic trade-off assessment, and practical solution design
At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone - of every race, gender, sexuality, age, location and income - deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.
Skills Required
- Graduate degree or equivalent experience
- Bachelor's degree or higher in Computer Science, Engineering, Data Science, Statistics, Mathematics, or a related field
- 4+ years of experience in software engineering, data science, analytics, or related fields, including 2+ years of hands-on AI/ML engineering experience
- Experience with deep learning architectures including feed-forward networks, CNNs, RNNs/LSTMs, attention mechanisms, and Transformers
- Experience applying algorithms to NLP, computer vision, recommender systems, reinforcement learning, and generative AI use cases
- Experience developing and deploying NLP, large language model, retrieval-augmented generation, and generative AI solutions
- Hands-on experience designing Agentic AI solutions using LangGraph, LangChain, AutoGen, Semantic Kernel, or CrewAI
- Experience building agent workflows with planning, reasoning, tool and function calling, task delegation, memory, context management, and human-in-the-loop controls
- Experience with LLMOps/MLOps practices, including experiment tracking, dataset, prompt, and model versioning, automated testing, monitoring, deployment, and rollback
- Experience developing production services with FastAPI, Flask, REST APIs, Streamlit, Docker, and Kubernetes
- Experience deploying data pipelines, models, or AI applications on Azure, AWS, or GCP
- Experience with Azure, Azure Machine Learning, and Databricks
- Solid understanding of supervised and unsupervised learning algorithms
- Knowledge of feature engineering, sampling, regularization, hyperparameter optimization, cross-validation, class-imbalance handling, explainability, and model-selection techniques
- Ability to select and interpret evaluation metrics including precision, recall, F1 score, ROC-AUC, PR-AUC, log loss, RMSE, MAE, and ranking metrics
- Solid proficiency in Python and PySpark
- Hands-on experience using Scikit-learn, TensorFlow or PyTorch, Pandas, NumPy, and MLflow
- Familiarity with open-source and commercial LLMs, Hugging Face, model APIs, prompt engineering, embeddings, vector databases, and retrieval architectures
- Ability to evaluate and secure AI systems using observability, guardrails, responsible AI practices, hallucination mitigation, retrieval-quality assessment, and performance and cost optimization
- Excellent analytical and problem-solving skills, including root-cause analysis, algorithmic trade-off assessment, and practical solution design
Optum Compensation & Benefits Highlights
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Leave & Time Off Breadth — Paid time off is often regarded as generous, with meaningful accrual and flexibility to use it. While practices can vary by team, the overall time-off offering stands out as a relative strength.
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Retirement Support — A 401(k) plan with employer matching is available and generally well-regarded for supporting long-term savings. It remains valuable, though candidates should verify the current match formula.
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Parental & Family Support — Programs such as parental leave, family medical leave, and adoption assistance are included as notable elements of the total rewards package. These resources can meaningfully support work-life balance when applicable to the role and location.
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The Company
What We Do
Optum, part of the UnitedHealth Group family of businesses, 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. At Optum, we support your well-being with an understanding team, extensive benefits and rewarding opportunities. By joining us, you’ll have the resources to drive system transformation while we help you take care of your future. We recognize the power of connection to drive change, improve efficiency and make a difference in health care. Join a team where your skills and ideas can make an impact and where collaboration is key to creating technology that produces healthier outcomes.
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Optum Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
Optum has three workplace models that balance the needs of the business and the responsibilities of each role. These models, core on‑site (5 days/week), hybrid (4 days/week) and telecommute or fully remote, vary by country, role and location.
Typical time on-site:
Not Specified
