Backend Engineer

6 days ago

Lahore, Punjab, Pakistan Stafflink Full time 800,000 - 2,400,000 per year

Your Responsibilities

  • Build
    RAG pipelines
    and
    agentic RAG workflows
    using
    LangGraph, LangChain, and PydanticAI (Python)
    .
  • Develop robust
    document ingestion
    (PDF/Office), chunking/metadata handling, and indexing against vector databases (
    Pinecone, Weaviate, pgvector
    ).
  • Integrate with
    Microsoft SharePoint
    and
    Google Drive/Docs
    (OAuth, webhooks, file sync, permissions).
  • Set up production pipelines on
    Railway
    .
  • Write
    unit/integration tests
    , enforce coding standards, and make sustainable
    architecture decisions
    .
  • Collaborate closely with founders and product teams, contributing to
    technical roadmaps
    and
    delivery planning
    .

Requirements (5+ years relevant experience)

  • Fluent in
    English
    (spoken & written).
  • Proven track record in
    backend engineering
    and building
    production systems
    .
  • Hands-on experience with
    AI/ML in Python
    , including
    RAG
    (preferably
    agentic RAG
    ).
  • Practical experience with
    LangGraph
    and/or
    LangChain
    , plus
    PydanticAI
    .
  • Experience deploying and operating on
    Railway
    .
  • Strong API skills with
    SharePoint
    and/or
    Google ecosystem
    integrations.
  • Solid knowledge of
    PostgreSQL
    ,
    S3-compatible storage
    , and
    vector databases (Pinecone, Weaviate, pgvector)
    .
  • Excellent communication skills, independence, and prior
    startup experience
    (or equivalent responsibility).
  • Strong expertise in
    embedding strategies
    and
    LlamaIndex
    .

Nice to Have

  • Document NLP
    (layout-aware parsing, OCR, table extraction).
  • Knowledge of
    security & compliance
    (GDPR, ISO practices), role/permission models.
  • Experience with
    multi-agent systems
    , workflow orchestration, and evaluation frameworks.

Our Tech Stack & Tools

  • Backend:
    Go (primary)
  • AI/Agents:
    Python, LangGraph, LangChain, PydanticAI, RAG/Agentic RAG
  • Infrastructure:
    Railway
  • Integrations:
    Microsoft SharePoint, Google Drive/Docs APIs
  • Data/Indexing:
    Vector DBs (Pinecone, Weaviate, pgvector), S3-compatible storage, LlamaIndex

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