Embedding Retrieval

5 days ago

Islamabad, Islamabad, Pakistan InterCraft Pvt. Ltd. Full time 900,000 - 1,200,000 per year

About the Role

We are seeking two experienced engineers to join our AI infrastructure team, focusing on embedding-based retrieval systems and agent orchestration frameworks. You will design, implement, and maintain components that enable intelligent agents to interact with APIs, external systems, and runtime environments using vector-based semantic search and secure orchestration logic.

This role is ideal for engineers who thrive at the intersection of LLM infrastructure, search systems, and agentic automation.

Key Responsibilities

Vector Search & Retrieval

  • Design and maintain vector databases (e.g., Pinecone, Weaviate, FAISS) for fast and scalable semantic search.
  • Build and fine-tune embedding pipelines using models like OpenAI, Cohere, Hugging Face, etc.
  • Implement and optimize retrieval-augmented generation (RAG) workflows for downstream LLM applications.

Agent Frameworks & Orchestration

  • Develop modular agent frameworks that support task delegation, contextual decision-making, and tool/API integrations.
  • Orchestrate agent behaviors for multi-step tasks, leveraging state machines, workflows, or planning-based architectures.
  • Integrate third-party and internal APIs with agents to enable dynamic task execution.

Security & Systems Integration

  • Ensure secure agent interactions with runtime environments, external APIs, and enterprise systems.
  • Manage authentication, permissioning, and sandboxing where necessary to maintain data and system integrity.

Required Qualifications

  • 3–5 years of experience in backend or ML engineering, with a focus on AI/ML infrastructure, LLMs, or intelligent agents.
  • Strong knowledge of vector databases and embedding models for semantic search.
  • Experience building or contributing to agentic systems (LangChain, LlamaIndex, AutoGPT, etc.).
  • Solid programming skills in Python, with experience using frameworks like FastAPI, LangChain, or similar.
  • Familiarity with API integration, secure data handling, and multi-system orchestration.
  • Experience with cloud platforms (AWS, GCP, or Azure) and containerized environments (Docker, Kubernetes).

Preferred Qualifications

  • Hands-on experience with retrieval-augmented generation (RAG) systems.
  • Exposure to knowledge graphs, task planning, or LLM tool use frameworks.
  • Contributions to open-source agent frameworks or retrieval tools.
  • Understanding of secure data access, API rate limiting, and runtime isolation.

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