AI Engineer (Enterprise Systems)
About the Role
As an AI Engineer on our Enterprise Systems team, you will be embedded directly in the trenches of our most strategic enterprise accounts. You'll take AI from proof of concept to production, navigate the chaos of real customer environments, and make complex agentic systems actually deliver business outcomes.
This is a consulting and hands-on delivery role. You will write production-grade code, architect intelligent agent workflows, debug pipelines at midnight before a go-live, and then walk into a boardroom the next morning to explain what you built and why it matters.
You'll own the full arc — from discovery and design through deployment and adoption — and your success is measured by one thing: does the customer's business actually change because of the AI you delivered?
If you love AI deeply, want to get your hands dirty across the entire delivery lifecycle, and feel equally at home talking to a team and writing LLM orchestration logic, this role was written for you.
What You'll Do
Enterprise AI Delivery
- Embed with enterprise customers to understand their workflows, data environments, and operational constraints — and build AI solutions that fit their reality, not a sanitized sandbox
- Own implementations end-to-end: from scoping and solution design through integration, testing, deployment, and handoff
- Rapidly diagnose technical blockers — messy data, broken integrations, edge cases, legacy system quirks — and solve them yourself without waiting on a queue
Agentic AI Engineering
- Design, build, and orchestrate multi-step AI agents that automate complex workflows across enterprise systems
- Work with frameworks like LangGraph, LangChain, or similar to architect reliable, production-ready agentic pipelines
- Apply sound judgment about what should be automated vs. what requires human-in-the-loop, and design accordingly
- Continuously tune agent behavior based on real-world usage patterns you observe in the field
Technical Breadth
- Build custom integrations, connectors, and data pipelines to bridge enterprise tech stacks with AI infrastructure
- Work across the stack — APIs, vector databases, LLM APIs, cloud infrastructure, and front-end surfaces — to deliver complete, working systems
- Write clean, maintainable code that others can build on top of
Product & Feedback Loop
- Identify patterns across customer engagements that signal genuine product opportunities — and advocate for them with engineering and product teams with precision
- Contribute to building internal tooling and repeatable delivery assets (deployment templates, agent blueprints, evaluation frameworks) that make the next implementation faster
- Optionally: take ownership of building product features or internal tools that emerge from your field insights
Requirements
Must-Haves
- 3+ years of software engineering experience, with at least 1–2 years focused on AI/ML systems in production environments
- Hands-on experience building with LLMs (OpenAI, Anthropic, Gemini, or open-source models) — prompt engineering, RAG pipelines, fine-tuning, evaluation
- Experience designing and deploying agentic AI workflows — multi-step reasoning, tool use, memory, planning
- Strong programming skills in Python; comfortable with APIs, cloud services (AWS/GCP/Azure), and enterprise databases
- Proven ability to work directly with enterprise customers or technical stakeholders — you can translate complexity into clarity
- End-to-end ownership mindset: you're not done when the code ships; you're done when the customer succeeds
Strong Signals We'll Look For
- You've debugged a production AI system that was misbehaving inside a customer's environment and fixed it under pressure
- You've built something agentic that actually ran in production — not just a toy demo
- You have opinions about AI system design, and you can back them up with experience
- You've identified a customer problem that became a product feature
- You've shipped something end-to-end — a product, a tool, a system — that you designed yourself from scratch
The Kind of Person Who Thrives Here
- Genuinely obsessed with AI. You follow the field closely, experiment constantly, and have strong convictions about where it's going
- Builder-first. When you see a problem, your instinct is to build a solution — not write a requirements doc and wait
- Comfortable with ambiguity. Enterprise environments are messy. You adapt fast, learn from what you observe, and iterate in real time
- Outcome-driven. You measure success by customer impact, not lines of code or tickets closed
Low ego, high ownership. You'll do what it takes — including the unglamorous work — to get something across the finish line
Benefits
- Competitive Salary
- Flexible working hour
- Work from home
- Group health insurance
- You pick your equipment (Mac / Windows)
- Grab Food and Grab Transportation
- Free snacks & drinks (at the office)
- Pay 100% for Job-related Training Courses
- Free language course and certificate fee