Applied Machine Learning Scientist developing Generative AI and predictive ML solutions at TD, a major North American bank. Supporting model evaluation, deployment, monitoring, and responsible AI governance.
About the role
- Senior AI/ML Engineer building Generative AI, RAG, and agentic solutions for pharmaceutical Statistical Programming. Deploying secure, validated, production-ready AI applications with Python and AWS.
Responsibilities
- Design and develop AI-enabled solutions for Statistical Programming across multiple studies and use cases
- Build end-to-end Generative AI and agentic workflows covering data and metadata ingestion, retrieval, reasoning, tool use, code generation, execution, validation, and human review
- Develop RAG and knowledge-driven solutions integrating organizational standards, metadata, specifications, historical study assets, programming conventions, and approved knowledge sources
- Develop modular AI services, APIs, and reusable components, separating deterministic business rules from probabilistic AI/LLM reasoning and generation
- Implement AI reliability, reproducibility, and quality controls, including structured inputs/outputs, prompt and model versioning, validation rules, automated evaluation, regression testing, and quality checks
- Build traceability and human-in-the-loop capabilities for review, approval, feedback, exception handling, audit trails, and lineage
- Support deployment and LLMOps across development, testing, validation, and production environments, including Git/CI/CD, monitoring, logging, model and prompt lifecycle management, security, and performance/cost optimization
- Collaborate with Statistical Programming, Enterprise Architecture, IT/Cloud, Security, Validation, and Governance teams to transition proofs of concept into scalable enterprise solutions
- Evaluate emerging AI technologies and architectural patterns
- Other duties as assigned
Requirements
- Bachelor’s or master’s degree in computer science, Engineering, Artificial Intelligence, Data Science
- 5+ years of hands-on experience in software engineering, AI/ML engineering, data engineering, or related technical roles, with demonstrated experience building and deploying production-quality applications
- Hands-on experience developing Generative AI/LLM solutions, including RAG, prompt/context engineering, embeddings, vector search, structured outputs, tool/function calling, and agentic AI workflows
- Strong programming skills in Python
- Experience with modular design, APIs, Git, automated testing, CI/CD, and preferably containerized/cloud-based applications
- Experience working with AWS and familiarity with cloud-based AI/ML services, data storage, security/access controls, logging, and monitoring
- Understanding of AI reliability and evaluation concepts, including reproducibility, hallucination mitigation, validation, prompt/model versioning, automated evaluation, regression testing, traceability, and human-in-the-loop approaches
- Strong analytical and problem-solving skills
- Ability to work across technical and business teams
- Good communication and organizational skills
- Experience in pharmaceutical/biotechnology or regulated environments and familiarity with clinical data, Statistical Programming, SAS/R, CDISC/SDTM/ADaM, or GxP principles preferred but not required
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