AI Integrations Backend Engineer at Pear VC

Pittsburgh, Pennsylvania, United States

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Not SpecifiedCompensation

Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

AI, TechnologyIndustries

Requirements

1-3 years backend engineering experience (Python, FastAPI, Flask, or similar)
Proven experience designing API-first microservices and integrating with AI or ML systems
Experienced in designing and managing scalable data pipelines and storage solutions using tools like Postgres, Redis, Kafka, and Airbyte
Experience with Docker, GitHub Actions, and cloud providers (e.g. GCP, AWS)
Strong fundamentals in REST/gRPC design, authentication, and CI/CD
Familiarity with LLM tool-calling APIs, agentic orchestration frameworks, and MCP-based architectures
Experience with LangGraph, PydanticAI, MCP servers, or equivalent orchestration stacks
Knowledge of vector databases (FAISS, pgvector, Pinecone, Weviate)
Experience using LLM APIs such as OpenAI and Anthropic’s
Clear communication, strong documentation, and collaborative mindset
Bonus: Solid understanding of async programming, streaming APIs, and structured data handling
Bonus: Background in numerical or tabular data systems, or working with embeddings and ML inference pipelines

Responsibilities

Design and build core backend APIs using Python (FastAPI) to serve numeric intelligence functions like embedding generation, reasoning calls, and model inference endpoints
Integrate our engine with LLM agents and tool-calling interfaces (OpenAI, Anthropic, Gemini) to enable structured reasoning over numeric data
Develop microservices and a Model Context Protocol (MCP) server, exposing modular “tools” that agents can securely invoke to process tabular or time-series data
Orchestrate agentic workflows using frameworks such as Vercel AI SDK, LangGraph, PydanticAI, or custom planners, and evaluate trade-offs in performance and observability
Build and maintain a Python SDK with clean abstractions and developer-first ergonomics
Develop data connectors for major environments such as Databricks, Snowflake, Postgres, and S3/GCS
Implement auth, rate limiting, usage metering, and structured logging for reliable production operations
Containerize and deploy microservices via Docker, GitHub Actions, and GCP/AWS, ensuring scalability and maintainability
Collaborate cross-functionally with ML and DX teammates to ensure seamless data flow and user experience

Skills

Python

FastAPI

microservices

LLM agents

OpenAI

Anthropic

Gemini

Model Context Protocol

MCP

Vercel AI SDK

LangGraph

PydanticAI

SDK development

backend APIs

embedding generation

agentic workflows

Pear VC

Venture capital firm for early-stage startups

About Pear VC

Pear VC specializes in providing venture capital for pre-seed and seed stage investments. The firm collaborates with founders to transform innovative ideas into successful companies across various sectors, including AI, healthcare, fintech, and more. Their investment strategy focuses on early-stage startups, offering not just financial support but also expertise and a strong network to help these businesses grow. Pear VC distinguishes itself from competitors through its hands-on approach, providing strategic advice and operational assistance to ensure startups remain agile and focused on their customers. The goal of Pear VC is to guide startups through their early development phases, helping them achieve significant growth and success.

San Francisco, CaliforniaHeadquarters

2013Year Founded

$2,738.4MTotal Funding

UNKNOWNCompany Stage

Venture Capital, Fintech, AI & Machine LearningIndustries

51-200Employees

Risks

Increased competition from other VC firms in AI and biotech sectors.

Potential overvaluation of startups could lead to investment losses.

Regulatory challenges in biotech and healthcare may impact growth strategies.

Differentiation

Pear VC specializes in pre-seed and seed investments across diverse sectors.

The firm offers hands-on strategic advice and operational support to startups.

Pear VC focuses on innovation in AI, fintech, SaaS, healthcare, and deep tech.

Upsides

Growing interest in AI-driven healthcare solutions presents new investment opportunities.

Rising demand for cell engineering platforms suggests lucrative biotech investments.

Fitness tech startups focusing on community building are gaining traction.

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