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Pinecone Vector Databases Jobs in Michigan (NOW HIRING)

Ford Motor Company

AI Software Engineer

Dearborn, MI

$100K - $192K/yr

Experience with vector search, hybrid retrieval architectures, or vector databases (Chroma, Qdrant, Pinecone, pgvector). * Experience working with GCP services (Vertex AI, Cloud Run, and BigQuery) or ...

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Pinecone Vector Databases information

What is a Pinecone Vector Database?

A Pinecone Vector Database is a cloud-based service designed to efficiently store, index, and search high-dimensional vector data, such as embeddings generated by machine learning models. It enables fast similarity search, making it ideal for use cases like semantic search, recommendation systems, and AI-powered applications. Pinecone handles the complexity of scaling and managing vector data, so developers can focus on building intelligent applications without worrying about infrastructure.

What are the key skills and qualifications needed to thrive as a Pinecone Vector Database Engineer, and why are they important?

To thrive as a Pinecone Vector Database Engineer, you need a strong background in computer science, data engineering, and experience with large-scale distributed systems, often supported by a relevant degree or equivalent experience. Proficiency in Python, REST APIs, cloud platforms (AWS, GCP), and vector search technologies, along with familiarity with Pinecone’s SDK and database management, are commonly required. Strong analytical thinking, problem-solving abilities, and effective communication skills help you collaborate with cross-functional teams and deliver scalable solutions. These skills ensure robust database performance, efficient data retrieval, and successful integration of vector search capabilities into real-world applications.

What are some common challenges faced by engineers working with Pinecone Vector Databases, and how can they be addressed?

Engineers working with Pinecone Vector Databases often encounter challenges such as optimizing vector search performance at scale, ensuring data consistency across distributed systems, and integrating the database with various machine learning pipelines. Addressing these challenges typically involves tuning indexing parameters, monitoring resource utilization, and collaborating closely with data scientists to understand retrieval requirements. Regularly reviewing documentation and participating in community forums can also help engineers stay current with best practices and new features.

What is the difference between Pinecone Vector Databases vs Data Engineers?

AspectPinecone Vector DatabasesData Engineers
Primary RoleManaging and deploying vector database solutions for AI/ML applicationsDesigning, building, and maintaining data pipelines and infrastructure
Skills & CertificationsKnowledge of vector databases, cloud platforms, programming (Python, SQL)Data modeling, ETL processes, cloud services, programming (Python, Java)
Work EnvironmentTech companies, AI startups, cloud providersData-driven organizations, tech firms, finance, healthcare

While Pinecone Vector Databases specialists focus on deploying and managing vector database solutions for AI applications, Data Engineers build and maintain the data infrastructure that supports these systems. Both roles require programming skills and familiarity with cloud platforms, but their core responsibilities differ: one centers on database management, the other on data pipeline development.

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Pinecone Vector Databases jobs near you

AI Agentic Engineer

Tata Consultancy Service Limited

Detroit, MI • On-site

$100K - $120K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 18 days ago

Job description

Must Have Technical / Functional Skills
• Core Engineering & Programming: Strong software engineering fundamentals with expert-level proficiency in Python. Experience with Java, Go, or TypeScript is a strong plus.
• LLM & GenAI Application Development: Proven, hands-on experience building and deploying production-grade applications using Large Language Models (LLMs) like GPT, Claude, or Gemini.
• This must go beyond simple API calls and include experience with tool/function-calling, structured outputs, and evaluation.
• Agentic AI Frameworks & Orchestration: Demonstrable expertise in designing and implementing agentic workflows using frameworks like LangGraph, Semantic Kernel, AutoGen, or similar.
• Experience with multi-agent systems, planning, and autonomous execution is critical.
• RAG (Retrieval-Augmented Generation): Deep, practical knowledge of building and optimizing RAG pipelines. This includes data ingestion, various chunking strategies, embeddings, vector databases (e.g., Pinecone, Chroma, FAISS), and hybrid search/reranking.
• Production & MLOps: Experience with production engineering practices, including building scalable APIs (REST, RPC), microservices, CI/CD pipelines, containerization (Docker, Kubernetes), and cloud platforms (AWS, Azure, or GCP).
Roles & Responsibilities
• Agentic System Design & Engineering: Architect, build, and deploy advanced AI agents capable of autonomous reasoning, decision-making, and self-directed task execution. Design and implement complex, multi-step agentic workflows that integrate with enterprise APIs, data sources, and platforms.
• RAG and Grounding Implementation: Develop robust RAG pipelines to ground agent responses in factual, reliable data. This includes managing the full lifecycle from data ingestion and vectorization to retrieval and citation.
• Tooling and Integration: Build and maintain the "tools" that agents use to interact with the digital world. Create secure, well-documented tool interfaces for internal services, databases, and third-party APIs.
• Evaluation, Guardrails & Safety: Design and implement comprehensive evaluation frameworks to measure agent performance, accuracy, and reliability.
• Develop and enforce safety guardrails, policy checks, and fallback mechanisms to ensure agents operate safely and predictably in production environments.
• Optimization and Productionization: Debug, monitor, and optimize agentic systems for latency, cost, and efficiency. Own the end-to-end deployment process, including CI/CD, structured logging, and incident response for AI systems.
Generic Managerial Skills, If any
• Problem-Solving & Critical Thinking: Ability to analyze complex, ambiguous problems and design innovative, practical solutions. Thrives in navigating the uncertainty inherent in emerging AI technologies.
• Collaboration & Communication: Excellent communication skills with the ability to articulate complex technical concepts to both technical and non-technical stakeholders. Proven experience working cross-functionally with product, research, and infrastructure teams.
• Ownership & Leadership: A bias for action and a strong sense of ownership. Capable of driving projects from conception to completion, mentoring junior engineers, and helping to define and influence AI strategy and best practices
Base Salary Range : $100,000 to $120,000 Per Annum
TCS Employee Benefits Summary:
Discretionary Annual Incentive.
Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.
Family Support: Maternal & Parental Leaves.
Insurance Options: Auto & Home Insurance, Identity Theft Protection.
Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement.
Time Off: Vacation, Time Off, Sick Leave & Holidays.
Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.
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