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
Hybrid Working
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

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