AI Solution Engineer

Job Brief: AI Solution Engineer

As an AI Solution Engineer at Gigalabs (Pvt) Ltd., you will be instrumental in developing innovative AI-driven solutions. This full-time, on-site role focuses on leveraging large language models (LLMs) to automate complex business logic, enhance data quality, and optimize database performance. You will be building robust internal tools and APIs, ensuring the seamless integration of AI into our operational workflows.

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 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.

Key skills/competency:

• Python
• FastAPI
• LLM Pipelines
• Prompt Engineering
• PostgreSQL
• Vector Databases
• n8n
• OpenAI API
• Microservices
• Data Quality