Senior Agentic AI Engineer

Senior Agentic AI Engineer role focused on building autonomous, enterprise-grade AI agents.

Responsibilities

• Design and deploy scalable multi-agent AI systems, including agent workflows, A2A communication protocols, and task delegation hierarchies for cooperative agent fleets
• Integrate LLMs with enterprise systems using APIs, function calling, and the Model Context Protocol (MCP) to enable reasoning, planning, tool use, and autonomous action
• Build and operate enterprise Agent Factory pipelines to evolve from reactive, episodic analytics to continuously executing intelligent workflows
• Develop retrieval-augmented generation (RAG) systems using ONNX-based embeddings and vector databases for context-aware, hyper-personalized outputs at scale
• Implement agent memory and state architecture, including short-term, medium-term, and long-term memory (for reliable multi-step execution using tools such as LangGraph)
• Own the agent and model lifecycle: feature engineering, training, production deployment (including Cloud Run and Vertex AI), and continuous monitoring with observability tooling
• Establish security guardrails, permission boundaries, short-lived agent identity tokens, and safety constraints for production agentic systems
• Develop and maintain propensity models, classification systems, and forecasting solutions that inform downstream agent decision-making
• Translate complex agentic AI outputs into clear operational strategies and present findings to senior leadership and cross-functional business partners
• Perform advanced AI-augmented analytics and generative AI model validation to support accuracy, reliability, and measurable business performance

Requirements

• Bachelor's degree or 4+ years of work experience
• 4+ years of relevant experience required, demonstrated through one or a combination of work and/or military experience, or specialized training
• 4+ years of relevant experience, with 2+ years specifically focused on Generative AI, LLMs, and autonomous agent systems
• 4+ years experience developing and implementing analytical or AI solutions to complex business problems
• Hands-on proficiency with multi-agent orchestration frameworks: LangChain, LangGraph, Google Agent Development Kit (ADK), LlamaIndex, or AutoGen
• Experience designing agent-to-agent (A2A) communication protocols, task delegation hierarchies, and the Model Context Protocol (MCP) for enterprise tool integration
• Experience with agent memory and state management: short-term context, long-term storage, context window optimization, and stateful workflow execution
• Demonstrated knowledge of RAG, ONNX-based embeddings, and vector databases (Pinecone, Weaviate, pgvector) plus semantic search
• Demonstrated knowledge of cloud-scale data engineering: BigQuery pipelines and GCP (including Cloud Run and Vertex AI), and experience with Amazon Bedrock or an Azure equivalent
• Experience with Python and SQL for statistical modeling, prompt engineering, token management, and large-scale data extraction

Technologies

• LangChain, LangGraph, Google Agent Development Kit (ADK), LlamaIndex, AutoGen
• Model Context Protocol (MCP), APIs, function calling
• Cloud Run, Vertex AI
• ONNX, Pinecone, Weaviate, pgvector
• Retrieval-augmented generation (RAG), vector databases, semantic search
• BigQuery, GCP, Python, SQL
• OpenTelemetry, Arize Phoenix, Galileo

Even Better If You Have

• Master's degree in a quantitative discipline such as Data Science, Computer Science, Statistics, Mathematics, Engineering, or Operations Research
• Familiarity with ML/LLM monitoring and observability tooling (e.g., OpenTelemetry, Arize Phoenix, Galileo) for tracking model drift, accuracy degradation, and agent output quality
• Experience with generative AI-augmented analytics: AI testing frameworks, model validation pipelines, and LLMOps tooling including observability and token management
• Experience with AI governance, security, compliance, and financial ROI modeling for AI agent deployments
• Domain expertise in customer, churn prediction, customer lifetime value (CLV) modeling, or related commercial analytics in Consumer, Telecommunications, Financial Services, or Technology industries
• Experience educating and communicating AI findings with integrity from raw output review to executive-facing AI strategy and business impact
• High curiosity, investigative mindset, and flexibility to adapt while staying focused on team deliverables

Where You’ll Be Working

• Hybrid role: defined work location with working from home and a minimum of three days per week in the office, set by your manager
• Employees are responsible for maintaining compliance with hybrid work policies

Schedule

• 40 hours weekly

Compensation & Benefits

• Salary range: USD 101,000 - 194,000 per year (varies by location and confirmed job-related skills and experience)
• Incentive based position with potential to earn more
• Health and wellness benefit options: medical, dental, vision
• Short and long term disability
• Basic life insurance
• Supplemental life insurance
• AD&D insurance
• Identity theft protection
• Pet insurance
• Group home & 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
• Premium pay such as overtime, shift differential, holiday pay, allowances, etc.
• Newly hired employees receive up to 15 days of vacation per year, which grows with additional service