AI Solution Engineer (Agentic & Prompt)
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Location
In office (Lahore, 12pm - 9pm)
Seniority
Middle · 3+ years exp
Confirmed on the employer's own hiring board on Oct 17, 2026. First seen by Alion on Oct 16, 2026.
Overview
Company
Impact
Profile match
Gigalabs provides custom software solutions with a focused tech stack and a client-centric approach to deliver impactful results across various industries.
Job Brief: Employment Type: Full time, Onsite Timings: 12PM-9PM Location: Johar Town, G4 Experience: 3+ 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 and
backend 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, window
functions, 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, conditional
workflows 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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