AI Engineer Lead

Allstate Allstate · Insurance · Chicago, IL

AI Engineer Lead at Allstate Investments responsible for guiding AI/ML initiatives from proof of concept to scalable, production-ready solutions. This role involves hands-on development with LLMs, RAG, vector stores, and agentic frameworks, partnering with product and platform leaders to build AI systems. Key responsibilities include establishing AI engineering fundamentals, participating in experimentation and productization, and iterating on PoCs.

What you'd actually do

  1. Play a key role in the design, development, and deployment of AI/ML solutions.
  2. Lead the transition of AI/ML initiatives from proof of concept to scalable, production-ready solutions.
  3. Establish and socialize strong AI engineering fundamentals, design patterns, code quality standards.
  4. Participate in projects, demonstrating technical expertise and a hands-on approach in experimentation and productization.
  5. Partner with product stakeholders to iterate quickly on PoCs, incorporating feedback and refining solutions in short cycles.

Skills

Required

  • Python development
  • AI projects
  • productizing AI/ML solutions
  • machine leaning fundamentals
  • GenAI technologies and techniques
  • RAG
  • fine-tuning
  • vector stores
  • Agentic frameworks
  • Data-driven decision making
  • analytical thinking
  • business judgment
  • communication with technical and non-technical team members
  • AI/ML experimentation
  • model development
  • software development principles
  • design patterns
  • testing
  • version control
  • Git
  • Python
  • APIs
  • pipelines
  • AI orchestration
  • LLMs
  • Prompting patterns
  • structured outputs
  • Model evaluation
  • accuracy/faithfulness checks
  • hallucination mitigation
  • regression testing
  • Safety/guardrails patterns
  • enterprise use
  • GitHub Copilot
  • code scaffolding
  • services
  • APIs
  • development
  • SQL
  • SQL Server
  • Query authoring
  • joins
  • indexing awareness
  • data access patterns for AI
  • LLMs (commercial)
  • Prompting patterns
  • structured outputs
  • RAG architecture
  • ingestion pipelines
  • chunking strategies
  • Vector database
  • vector search
  • Indexing
  • similarity search
  • metadata filtering
  • MCP patterns
  • Standardizing tool/context access for models
  • agent runtimes
  • MCP servers/tool endpoints
  • LLM apps
  • agentic workflows
  • A2A patterns
  • multi-agent collaboration
  • planner/executor/reviewer/retriever roles
  • MS Azure Fabric
  • Logging
  • metrics
  • tracing
  • APM tools
  • Datadog
  • Model/prompt monitoring
  • drift signals
  • quality regression detection
  • Bachelors Degree

Nice to have

  • Java
  • ReactJS
  • Microsoft Fabric
  • Solid grasp of software development principles, including design patterns, testing, and version control such as Git.
  • Inquisitive nature, with a deep understanding of both technical and business aspects.

What the JD emphasized

  • productizing AI/ML solutions
  • Hands-on experience with LLMs
  • implementing RAG architecture
  • Vector database / vector search experience
  • Experience implementing agentic workflows
  • Experience operating and supporting AI-enabled services in production

Other signals

  • productizing AI/ML solutions
  • guide successful PoCs into scalable, production-ready AI solutions
  • Deploying AI/ML solutions
  • transition of AI/ML initiatives from proof of concept to scalable, production-ready solutions
  • productization
  • implementing RAG architecture
  • implementing agentic workflows