AI Engineer – Enterprise Systems

Role Overview

• 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
• 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
• 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
• 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

• 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

Tech Stack

• AWS
• Azure
• Cloud
• Google Cloud Platform
• Python

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