Role Overview:Design, engineer, and implement enterprise-scale AI/ML and generative AI solutions for clinical data workflows. This role requires delivering secure, scalable architectures independently while maintaining accountability for project delivery and technical excellence.
Key Responsibilities:- Conceive, design, and implement AI solutions; analyze workflows and devise innovative technical approaches.
- Design secure, scalable architectures for AI/ML, generative AI, and agentic solutions.
- Design and implement emerging AI technologies such as RAG, agentic workflows, and agent-to-agent communication.
- Build and deploy predictive analytics and generative AI solutions to production environments.
- Develop robust data/model pipelines, APIs, and integration layers; establish AI/MLOps best practices.
- Implement CI/CD, monitoring, and observability for AI/ML systems.
- Own project scope and delivery accountability.
Required Skills:- Cloud Platforms: AWS (SageMaker, EC2, S3, Lambda, RDS, Glue, Athena, DynamoDB, Postgres), Databricks (Platform, Delta Lake, Spark, MLflow, SQL).
- Programming Languages & ML: Python, PySpark, SQL.
- DevOps & Infrastructure: Git, CI/CD, Docker, Kubernetes, IaC (Terraform/CloudFormation).
- AI/Data Technologies: Generative AI frameworks, Large Language Models (LLMs), vector databases, Apache Spark.
Qualifications:- Bachelor's degree (BS) in Computer Science, Engineering, Mathematics, Statistics or equivalent professional experience.
- 5+ years of experience in software, data, or ML engineering.
- 3+ years of experience deploying ML solutions in production environments.
Preferred Skills:- Digital Artificial Intelligence (AI) expertise.
