Principal Data Scientist, Agentic AI Technical Lead
New
J
JobgetherAgentic AI
Based in CanadaFull-TimePrincipal
Salary not disclosed
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Job Details
- Experience
- 12+ years of professional experience in data science and AI/ML
- Required Skills
- AWSPythonMLOpsDistributed Systems
Requirements
- Have 12+ years of professional experience in data science and AI/ML.
- Have a proven record of taking AI systems from research or experimentation into production at enterprise scale.
- Have experience leading technical delivery across multiple concurrent workstreams, teams, or large AI programs.
- Demonstrate experience managing technical dependencies, risks, architectural decisions, and delivery quality across complex initiatives.
- Bring hands-on experience with LLM and agentic AI systems, including prompt engineering, tool and function calling, multi-agent orchestration, RAG, vector databases, embeddings, and streaming LLM responses.
- Have strong expertise in model evaluation, experimentation design, applied statistics, and evaluation methods for generative and agentic AI.
- Have advanced Python skills and proficiency across modern data science and machine learning technologies.
- Have extensive MLOps and AI infrastructure experience, including model versioning, monitoring, deployment automation, reproducibility, and production operations.
- Have strong software engineering fundamentals, including system design, API design, code quality, testing, and maintainable architecture.
- Have experience with distributed systems, event-driven architectures, and workflow orchestration technologies.
- Have in-depth AWS experience, particularly with Amazon Bedrock, and working knowledge of other cloud platforms.
- Be familiar with SQL and NoSQL databases, scalable data-storage and access patterns, AI governance, responsible AI, safety, and compliance.
Responsibilities
- Provide technical oversight across agentic AI workstreams from problem definition and architecture through evaluation, deployment, and ongoing operation.
- Define technical strategy and reference architecture for agentic AI systems, including orchestration, tool calling, RAG, vector databases, embeddings, and streaming LLM responses.
- Coordinate technical delivery across teams and workstreams, managing dependencies, blockers, technical risk, quality, and velocity.
- Establish standards for model development, experimentation, evaluation, and production deployment.
- Design evaluation frameworks for model and agent quality, safety, hallucination, cost, latency, and performance.
- Guide scalable ML platforms, pipelines, event-driven architectures, and workflow orchestration systems.
- Ensure deployed AI systems meet reliability, security, scalability, observability, monitoring, and production-debugging standards.
- Partner with executives and stakeholders on the AI roadmap, communicating progress, risks, trade-offs, and technical decisions.
- Review technical work, provide feedback, and mentor technical leads and senior practitioners.
- Use AI-assisted development tools and workflows to improve productivity, engineering quality, and delivery speed.
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