Pinecone Vector Databases Jobs in Michigan (NOW HIRING)

We are moving beyond "Chatbots." We are building AI-native applications where LLMs aren't just features-they are the core engine. As a AI Full Stack Engineer, you will architect systems where autonomous agents navigate complex business logic, utilize the Model Context Protocol (MCP) to interact with live data, and provide users with seamless, real-time streaming experiences.
You aren't just a software engineer; you are an AI Orchestrator bridging the gap between non-deterministic model logic and high-performance software engineering.

• BS in Computer Science or Engineering related field 
• 5+ years of total software engineering experience
• 2+ years focused on AI integration
• Technical Requirements:
  • AI Frameworks: 2+ years of professional experience with LangChain, LlamaIndex, or Google ADK.
  • AI Development Tools: GitHub Copilot (IDE & CLI), Claude Code, Cursor, or similar agentic coding assistants.
  • System Design: Ability to architect end-to-end AI-native systems-covering data flow, latency budgets, failure modes, scaling strategies, and LLM integration patterns.
  • Back-end Mastery: 3+ years designing and building Python (FastAPI/Flask) or Java (Spring Boot) services, with expertise in async patterns, distributed systems, and API design.
  • Front-end Precision: 2+ years designing real-time, streaming UI systems in React or Angular, with a focus on state management, WebSocket/SSE patterns, and component architecture.
  • Data Architecture: Expert knowledge of SQL (PostgreSQL/pgvector) and Vector Databases (Chroma, Qdrant, or Pinecone).
  • Cloud Infrastructure: Hands-on experience with GCP (Vertex AI) or AWS (Bedrock).
  • Modern DevOps: Experience with Git, Docker, Kubernetes,Terraform, and CI/CD (GitHub Actions/Jenkins).
• What Sets You Apart:
  • The Agentic Mindset: You have a proven track record of moving models from "answering questions" to "completing multi-step tasks."
    Prompt Engineering as Code: You treat prompts as production code-versioned, tested, and optimized for deterministic outcomes.
  • MCP Expertise: You understand the future of data grounding and have experimented with or implemented Model Context Protocol servers.
  • Growth Agility: You stay ahead of the curve, moving fluently from RAG-based architectures to Long-Context model strategies as the landscape shifts.

• Immediate medical, dental, vision and prescription drug coverage

• Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more

• Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more

• Vehicle discount program for employees and family members and management leases

• Tuition assistance

• Established and active employee resource groups

• Paid time off for individual and team community service

• A generous schedule of paid holidays, including the week between Christmas and New Year's Day

• Paid time off and the option to purchase additional vacation time.

1. Agentic Orchestration & Logic
Design Autonomous Loops: Transition from linear "chain" workflows to self-correcting agentic loops using frameworks like LangChain, LlamaIndex, or CrewAI.
Tool-Use Architecture: Design and implement robust "tool-calling" capabilities, ensuring LLMs can reliably interact with external APIs and microservices.
MCP Integration: Build and maintain Model Context Protocol (MCP) servers to bridge the gap between LLMs and our proprietary data silos securely and in real-time.

2. Full Stack AI Delivery
High-Concurrency Back-end: Develop asynchronous Python (FastAPI) or Java services optimized for long-running AI tasks and token streaming.
Streaming Front-end: Build responsive, stateful UIs in React or Angular that handle complex AI interactions (streaming text, generative UI components, and multi-modal feedback).
Advanced RAG Pipelines: Go beyond basic vector search. Implement re-ranking, query transformation, and embedding optimization to maximize retrieval precision.

3. AI Engineering Excellence
Context Optimization: Master the "Context Window" by implementing prompt compression and "lost-in-the-middle" mitigation strategies.
Evaluation & Observability: Establish AI Evals to quantify hallucination rates, latency, and cost. Lead the shift from "vibes-based" testing to rigorous, automated AI benchmarking.
DevOps/MLOps: Manage CI/CD pipelines that include vector database migrations and automated prompt versioning.