Job Description: Your Role:
Join *** Technologies as a Senior AI Engineer where you will design, implement, and productionize advanced AI solutions that improve clinical workflows, patient experience, and operational efficiency across the healthcare system. Working within an Agile team, you will translate clinical and business needs into robust machine learning and AI systems, own model lifecycle delivery, and partner with product, data engineering, SRE, and clinical SMEs. This role emphasizes engineering excellence, reproducible experiments, compliance with healthcare regulations, and mentoring junior engineers.
Key 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.
Essential Qualifications:
- 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.
Preferred Additional Experience:
- 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.
Core Competencies & Behaviors:
- Strong product orientation and ownership of end-to-end delivery.
- Collaborative, mentorship mindset and experience leading cross-functional initiatives.
- Systems thinking and pragmatic trade-off analysis (performance, cost, reliability, interpretability).
- Commitment to quality, reproducibility, and compliance in an enterprise/clinical setting.
- Open-mindedness and an enduring eagerness to learn new techniques and tools.
Fully remote, team works in CST
#LI-NorthAmerica
Where required by law, NTT DATA provides a reasonable range of compensation for specific roles. The starting pay range for this role is $73-$77/hour. Actual compensation will depend on several factors, including the candidate’s relevant experience, technical skills, and other qualifications. This position may also be eligible for incentive compensation based on individual and/or company performance.
This position is eligible for company benefits including participation in medical, dental, and vision insurance, flexible spending or health savings account, and AD&D insurance, employee assistance, participation in a 401k program, and additional voluntary or legally-required benefits.