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Open nowFirst seen 3 days ago

AI Solution Engineer (Agentic & Prompt)

Gigalabs (Pvt) Ltd.1 open role

Where
Lahore, Pakistan
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Your applicationOpen nowAI Solution Engineer (Agentic & Prompt)Gigalabs (Pvt) Ltd. · Lahore, Pakistan
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The clock on this job

Early applications get read.

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Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: first seen 3 days ago

The posting

Job Brief: Employment Type: Full time, Onsite Timings: 12PM-9PM Location: Johar Town, G4 Experience: 4-5 years  Responsibilities:

  • Develop AI-Driven APIs: Build internal tools and endpoints using FastAPI that serve data insights powered by LLMs.
  • Automate Business Logic: Replace manual data entry and analysis tasks with autonomous n8n workflows.
  • Data Quality Assurance: Ensure the inputs fed into LLM pipelines are clean and the outputs generated are accurate and hallucination-free.
  • Database Management: Maintain and optimize the PostgreSQL instance to handle increasing loads of relational and vector data.

Requirements:

Candidates must possess hands-on proficiency in the following areas:

Core Programming & APIs

  • Python Proficiency: Advanced knowledge of Python for data manipulation andbackend logic.
  • API Development: Proven experience building, documenting, and maintaining RESTful APIs using FastAPI (preferred for async capabilities) and Flask.
  • Microservices: Ability to wrap data models and scripts into deployable services.

AI & LLM Integration

  • LLM Pipelines: Experience constructing chains and agents (e.g., using LangChain,LlamaIndex, or custom scripts) to process text and data.
  • OpenAI API: Deep familiarity with the OpenAI ecosystem (GPT-4, Assistants API, function calling) and cost optimization strategies.
  • Prompt Engineering: Demonstrated ability to design system prompts that yield consistent, structured outputs (JSON) from unstructured data.

Database & Data Management

  • PostgreSQL Mastery: Advanced SQL skills including complex joins, windowfunctions, and performance tuning.
  • Vector Data: Experience with vector embeddings and vector similarity search (e.g., using pgvector in PostgreSQL) for RAG (Retrieval-Augmented Generation) implementations.
  • Data Modeling: Ability to design efficient database schemas to store historical data and LLM interaction logs.

Automation & Orchestration

  • n8n Workflow Automation: Strong experience creating complex, conditionalworkflows in n8n to connect disparate services (Webhooks, APIs, Databases).
  • ETL/ELT: Ability to automate data extraction and transformation pipelines using n8n and Python scripts.
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