Senior AI Engineer

NTT DATA North America is looking for a Senior AI Engineer to design, implement, and productionize advanced AI solutions in healthcare. This role involves working within an Agile team to develop ML/AI systems, manage the model lifecycle, and collaborate with various stakeholders to improve clinical workflows and patient experiences.

Responsibilities:

• Design and develop production-grade ML/AI systems (classical ML, deep learning, LLM-based and RAG solutions) for healthcare use cases
• Lead end-to-end model lifecycle: data ingestion, feature engineering, model training, validation, deployment, monitoring and continuous improvement
• Work with data engineering to create robust, auditable, and HIPAA-compliant data pipelines and feature stores
• Implement MLOps best practices—model versioning, CI/CD for models, automated training pipelines, canary/blue-green rollouts, and drift detection
• Develop and execute model evaluation plans: performance, calibration, bias/fairness assessments, robustness, and clinical validation
• Ensure model explainability and privacy-preserving practices where appropriate (differential privacy, secure inference patterns)
• Expose models through well-documented APIs and microservices, optimizing for latency, cost, and reliability
• Collaborate with clinical SMEs and product teams to translate problems into technical requirements and measurable success metrics
• Perform code and architecture reviews; mentor and coach engineers and contribute to hiring and technical roadmaps
• Maintain reproducible experiments, model artifacts, and technical documentation; lead PoCs and vendor evaluations

Requirements:

• B.S. or M.S. in Computer Science, Data Science, Electrical Engineering, or equivalent experience; advanced degree (MS/PhD) preferred
• At least 7 years of professional software engineering experience with a minimum of 3–5 years focused on machine learning/AI and production deployments
• Strong software engineering fundamentals: OOAD, design patterns, data structures, algorithms, and clean/testable code practices
• Proficient in Python and ML/AI frameworks such as PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers
• Experience with MLOps and tooling (MLflow, Kubeflow, TFX, Seldon, or similar) and CI/CD systems for models and services
• Cloud experience (GCP/Azure) for training and serving models; containerization and orchestration (Docker, Kubernetes)
• Strong data skills: SQL, Spark/Databricks, feature stores, and production data engineering practices
• Hands-on experience with model monitoring, A/B testing, observability, and logging for ML services
• Excellent verbal and written communication skills; ability to explain complex technical concepts to clinical and business stakeholders
• Demonstrated ability to lead technical delivery and mentor other engineers in an Agile environment
• Prior experience in healthcare or other regulated industries; familiarity with HIPAA, clinical validation, and regulatory-compliant ML processes
• Experience building or deploying Large Language Models, retrieval-augmented generation (RAG), or multimodal models
• Background in privacy-preserving ML (federated learning, differential privacy) or safety/ethics frameworks for AI
• Cloud certifications (GCP/Azure) or formal MLOps/ML engineering certifications
• Comfort with observability stacks, metrics, alerting, and SRE-oriented production readiness for ML systems
• Familiarity with relational and NoSQL databases (MySQL, Postgres, MongoDB, Redis) and message/event systems (Kafka, RabbitMQ)
• Experience with Atlassian tools (JIRA, Confluence), and TDD/BDD practices