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Tech Stack
Tools & technologiesAzureBigQueryCloudGoogle Cloud PlatformPythonSQL
About the role
Key responsibilities & impact- Architect and deploy scalable multi-agent AI systems, including agent workflows, A2A communication protocols, and task delegation hierarchies
- Integrate LLMs with enterprise systems through APIs, function calling, and MCP
- Build and operate enterprise Agent Factory pipelines for continuously executing intelligent workflows
- Design and implement RAG systems with ONNX-based embeddings and vector databases
- Manage short-term, medium-term, and long-term agent memory and state architecture using tools such as LangGraph
- Own the agent and model lifecycle, including feature engineering, training, production deployment on Cloud Run and Vertex AI, and continuous monitoring
- Establish security guardrails, permission boundaries, short-lived agent identity tokens, and safety constraints
- Develop and maintain propensity models, classification systems, and forecasting solutions
- Translate agentic AI outputs into operational strategies for senior leadership and cross-functional business partners
- Perform advanced AI-augmented analytics and generative AI model validation for accuracy, reliability, and business performance
Requirements
What you’ll need- Bachelor's degree or four or more years of work experience
- Four or more years of relevant experience, demonstrated through work and/or military experience or specialized training
- Four or more years of relevant work experience, with two or more years focused on Generative AI, LLMs, and autonomous agent systems
- Four or more years of experience developing and implementing analytical or AI solutions to complex business problems
- Hands-on proficiency with LangChain, LangGraph, Google Agent Development Kit (ADK), LlamaIndex, or AutoGen
- Experience designing A2A communication protocols, task delegation hierarchies, and MCP for enterprise tool integration
- Experience with agent memory and state management, including short-term context, long-term storage, context window optimization, and stateful workflow execution
- Knowledge of RAG, ONNX-based embeddings, vector databases such as Pinecone, Weaviate, or pgvector, and semantic search
- Knowledge of BigQuery pipelines and GCP, including Cloud Run, Vertex AI, Amazon Bedrock, or Azure equivalent
- Experience with Python and SQL for statistical modeling, prompt engineering, token management, and large-scale data extraction
- Master's degree in a quantitative discipline such as Data Science, Computer Science, Statistics, Mathematics, Engineering, or Operations Research is preferred
- Familiarity with ML/LLM monitoring and observability tooling such as OpenTelemetry, Arize Phoenix, or Galileo
- Experience with generative AI-augmented analytics, AI testing frameworks, model validation pipelines, and LLMOps tooling
- Experience with AI governance, security, compliance, and financial ROI modeling for AI agent deployments
- Domain expertise in customer analytics, churn prediction, CLV modeling, or related commercial analytics in Consumer, Telecommunications, Financial Services, or Technology industries
- Ability to communicate AI findings to colleagues, business partners, security stakeholders, and executives
Benefits
Comp & perks- Medical, dental, and vision coverage
- Short- and long-term disability insurance
- Basic and supplemental life insurance
- AD&D insurance
- Identity theft protection
- Pet insurance
- Group home and auto insurance
- Matched 401(k) savings plan
- Up to 8 company-paid holidays per year
- Up to 6 personal days per year
- Paid parental leave
- Adoption assistance
- Tuition assistance
- Other incentives
- Potential premium pay such as overtime, shift differential, holiday pay, and allowances
- Up to 15 days of vacation per year for newly hired employees, increasing with additional service
- Hybrid work arrangement with working from home and a minimum of three days per week in the office