Architect a scalable, multi-tenant agentic AI data platform using the Azure technology stack.
Design a hybrid data architecture supporting operational systems, agentic AI workloads, and a knowledge graph
Build infrastructure utilizing vector and graph databases for RAG applications and semantic search
Design and implement comprehensive MLOps platform including deployment management, AI security and safety, and observability on Azure supporting the full ML lifecycle from experimentation to production.
Build automated pipelines using Azure technologies for continuous integration and deployment
Implement real-time inference infrastructure with monitoring, alerting, and automated drift detection
End-to-end lifecycle management including hydration from existing taxonomies/ontologies
Develop high-performance graph query services and APIs for real-time access to supply chain relationships
Deploy automated validation, conflict resolution, and data quality monitoring to ensure graph consistency and accuracy
Implement Infrastructure as Code (IaC) and build CI/CD pipelines for data products and ML models
Set and enforce platform standards for cost control, model/runtime selection, and performance targets (including budgeting, attribution, and optimization for training + inference).
Lead AI governance with Security/Legal/Privacy, turning policy into technical controls (access, auditability, guardrails, retention, and risk management).
Build scalable data + evaluation systems: synthetic dataset generation where appropriate, automated benchmarks, and release quality gates for RAG/agents as well as classical ML.
Create self-service capabilities with comprehensive monitoring and observability
Drive engineering delivery improvements through AI-based code analysis to improve quality, maintainability, and general code health.
Drive engineering efficiency improvements through best practices and adoption of AI-assisted coding tools and capabilities.
Stay on top of new research including cutting edge AI technology.
Democratize AI through TraceGains teams for adoption and deployment across the platform.

Requirements

Master's degree in Computer Science, Data Engineering, or related field (or equivalent experience), Ph.D. is a plus.
10+ years building enterprise data and AI platforms in production environments
2-3 years driving LLM adoption for Generative AI use cases
1+ year(s) of building agentic systems including deployment of MCP servers to enable agentic AI.
Proven ability to lead cost-aware AI delivery, technically grounded governance decisions, and large-scale evaluation/data practices.
Proven track record designing and implementing MLOps platforms
Experience with Pipelines, model monitoring, and drift detection
Experience with graph databases, and vector databases to support RAG
Ability to drive CI/CD for ML, security, safety, monitoring, and observability
Proven ability to establish shared platform capabilities that serve multiple product teams
Proven ability to deliver AI capabilities into production
Strong communication skills with ability to present to executive leadership
Track record of cross-functional collaboration with AI product teams, ML, and business stakeholders
Experience establishing technical standards and governance frameworks across distributed teams
Ability to be a team player, willing to grow and change and drive change into the organization in a positive and constructive manner.
Experience mentoring technical teams (data engineers, AI/ML engineers, and platform engineers)
Occasional travel required for department meetings, all company events, in-person seminars/networking events, etc.
Successful completion of a drug and background screening process.

Benefits

paid time off
medical/dental/vision insurance
401(k) to eligible employees

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AzureMLOpsgraph databasesvector databasesCI/CDInfrastructure as Code (IaC)real-time inferenceautomated pipelinesdata quality monitoringsynthetic dataset generation

Soft Skills

strong communication skillscross-functional collaborationteam playerleadershipmentoringcost-aware decision makingdriving changetechnical governancepresentation skillsadaptability

Certifications

Master's degree in Computer ScienceMaster's degree in Data EngineeringPh.D. (preferred)