Optum AI/ML Enngineering Job Alerts 2026 – Apply Soon!

Qualifications

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

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