Lead AI Engineer

About

In this role you will design and deploy AI-powered automation and intelligent agents that embed into business operations, using governed enterprise data and orchestration tools. You will partner with Data, IT and business teams to automate workflows, support decision-making, and boost operational efficiency. Focus is on applying AI to real processes, not research or model training, with a strong emphasis on production-grade reliability and observability. You will work with a cross-functional team to scale AI-enabled capabilities across the enterprise.

Responsibilities

• Build internal AI assistants and copilots for support, operations, and business teams
• Implement Retrieval Augmented Generation (RAG) using Snowflake data and curated metrics
• Ground AI responses using enterprise data and business logic
• Implement prompt strategies, guardrails, and response evaluation techniques
• Automate operational processes such as ticket triage, routing, approvals, and document handling
• Develop AI-driven classification, summarization, and recommendation services
• Implement human-in-the-loop workflows and exception handling
• Build and maintain AI-powered backend services, APIs, and microservices
• Integrate AI capabilities with ITSM, CRM, ERP, and internal apps
• Troubleshoot failures across data pipelines, orchestration, and model inference layers
• Participate in technical design and architecture discussions
• Utilize Snowflake as the trusted data source for AI decisions
• Use dbt models as the semantic and business logic context for automation
• Enable real-time and batch data-driven decision support
• Ensure AI actions align with defined business metrics and data definitions
• Implement serverless workflows using AWS (Lambda, Step Functions, API Gateway, S3, EventBridge)
• Monitor system performance, latency, and operational reliability
• Track AI usage, accuracy, and cost efficiency
• Implement logging, auditing, and traceability of AI decisions

Qualifications

• 5+ years of software or ML engineering experience
• 2+ years of building LLM systems in production
• Proficiency in Python; TypeScript or Go for full-stack AI applications
• Experience with production RAG systems using Snowflake Cortex Search, pgvector, hybrid search, and re-ranking
• Hands-on experience building MCP (Model Context Protocol) servers and clients
• Proven track record implementing AI observability
• Experience with LLM APIs and cloud platforms (OpenAI, Anthropic, Azure, Gemini; AWS SageMaker, Lambda, S3, Bedrock)
• Familiarity with CI/CD and MLOps tooling (MLflow, Weights & Biases, Snowflake ML Registry)
• Experience with responsible AI practices in live deployments (bias checks, output validation, human-in-loop escalation)
• Proactive evaluation and adoption of new AI tools or frameworks
• Experience managing production incidents and model rollbacks
• Snowpark or external functions in Snowflake
• Experience with enterprise SaaS platforms (ServiceNow, Salesforce)
• Workflow orchestration tools (Airflow, n8n)
• Authentication and access control concepts (OAuth, RBAC, SSO)
• Exposure to vector search or semantic retrieval technologies

Benefits

• Health, Dental, Life, STD, LTD
• 401K with company match
• PTO and holidays
• Stock purchase options
• Remote eligible with hybrid opportunities in select locations
• Relocation support