Associate – AI Automation & Integration Engineer

About The Role

A hands-on builder role at the intersection of AI engineering, automation, and systems integration. You will research, design, and ship AI-powered solutions that reduce manual effort, improve operational efficiency, and scale internal workflows. You work across multiple live projects simultaneously.

• AI Engineer — design and deploy LLM-driven pipelines, agents, and AI-integrated tools across business workflows.
• Automation Engineer — identify, build, and maintain automations that eliminate repetitive tasks and improve throughput.
• Integration Engineer — connect disparate systems, platforms, and APIs into cohesive, reliable workflows.
  
  

What You’ll Be Doing

AI Engineering & LLM Integration

• Integrate LLMs (GPT-4, Claude, Gemini, etc.) into internal tools and business workflows via API.
• Design and maintain prompt systems: system prompts, structured outputs, chain-of-thought pipelines.
• Build RAG pipelines — connect knowledge bases, spec documents, and SOPs to AI agents for accurate context retrieval.
• Evaluate and reduce hallucinations; implement human-in-the-loop validation where needed.
• Stay current on model releases, capabilities, and best practices — apply them immediately.
  
  

Automation & Workflow Engineering

• Map existing manual workflows; identify and prioritize automation opportunities.
• Build end-to-end automation pipelines for repetitive tasks: data extraction, form-filling, cross-platform data transfer.
• Develop browser and desktop automations using Playwright, Puppeteer, or equivalent computer-use frameworks.
• Create internal tools — small scripts, utilities, and micro-applications that save time and reduce errors.
• Maintain and iterate on existing automations as workflows evolve.
  
  

Systems Integration

• Connect third-party platforms, carrier portals, and SaaS tools via APIs, webhooks, and automation middleware.
• Build and maintain integration workflows using n8n, Make, Zapier, or Power Automate.
• Architect modular, maintainable systems — clean inputs, reliable outputs, clear documentation.
• Troubleshoot integration failures and maintain system reliability.
  
  

AI Research & Tool Evaluation

• Research and benchmark emerging AI tools, agent frameworks, and automation platforms.
• Evaluate feasibility of new tools for real use cases; produce concise internal assessment reports.
• Build reusable prompt libraries, automation templates, and internal knowledge bases.
• Contribute to AI governance practices: output validation, bias checks, ethical use.
  
  

TECH STACK & TOOLS

• AI & LLM Platforms
• OpenAI / GPT-4o
• Anthropic Claude
• Google Gemini
• Open-Source LLMs - OpenClaw
• Agent & Automation Frameworks
• LangChain / LangGraph
• AutoGen / CrewAI
• n8n
• Make (Integromat)
• Zapier
• Power Automate
• Playwright
• Puppeteer
• Development & Tooling
• Python
• REST APIs
• Git / GitHub
• Postman
• Claude Code
• VS Code
• JSON / YAML
• Basic JavaScript
• RAG & Knowledge Systems
• Vector DBs (Pinecone / Chroma)
• RAG Pipelines
• Document Parsing
• Embeddings APIs
  

What We’re Looking For

Education

• Bachelor’s degree in Computer Science, Software Engineering, Information Technology, Data Science, or Artificial Intelligence preferred.
• Equivalent demonstrated skills and a strong project portfolio are equally valued.
  
  

Core AI & Technical Skills

• Working knowledge of LLMs: context windows, token efficiency, model behavior, and limitations.
• Prompt engineering proficiency: zero-shot, few-shot, chain-of-thought, structured output, and agentic prompting.
• Experience calling AI APIs and building functional workflows around them.
• Python scripting for automation, API integration, and data handling.
• Hands-on experience with at least one automation platform (n8n, Make, Power Automate, or Zapier).
• Familiarity with RAG concepts, vector databases, and document-grounded AI systems.
• Basic understanding of AI agents, tool-use, and multi-step reasoning pipelines.
  
  

Advanced AI Knowledge (Good to Have)

• Awareness of latest model releases, benchmarks, and capability shifts across major AI providers.
• Exposure to agentic frameworks: LangGraph, AutoGen, CrewAI, or similar.
• Understanding of fine-tuning concepts, embeddings, and semantic search.
• Experience with computer-use or browser-control agents.
  
  

Mindset

• Research-first: actively follows the AI space, reads docs, tests new tools.
• Builder: ships working systems — not just plans them.
• Detail-oriented: meticulous about output quality, testing, and validation.
• Adaptable: comfortable with ambiguity and a fast-moving environment.
  
  

Experience

• Minimum 1 year of experience — skills and portfolio matter most.
• Personal projects, freelance work, coursework, or hackathon entries involving LLMs, agents, or automation all count.
  
  

KEY PERFORMANCE INDICATORS

• 30–60% reduction in manual processing time on automated workflows.
• Fast prototype-to-deployment velocity within sprint cycles.
• Consistent, low-error output quality across AI pipelines.
• High internal adoption rate and positive team feedback on built tools.
  
  

WORK MODEL

Hybrid Model: This position requires 4 weeks on-site for initial onboarding and training, followed by a transition to a standard hybrid schedule .

"We value the uniqueness and experience each individual brings to the organization."