Artificial Intelligence Engineer

Job Summary:

At Greystar-India, the AI Center of Excellence (COE) is reimagining how Finance & Accounting operates across our global enterprise — spanning property accounting, investment accounting, accounts payables, accounts receivables, contracts and audits, budgeting, procure-to-pay, order-to-cash, and financial planning and analysis. Not every solution we build will need an AI component — but where it does, the AI Developer is the craftsperson who brings it to life.

As an AI Developer, you will build the AI-powered components of our solutions — LLM-based extraction, classification, summarisation, NLP, OCR pipelines, RAG patterns, and intelligent automation. You will partner with the Business Analyst(s) to deeply understand the business problem, with the AI Architect to align on the technical design and reusable framework, and with our Automation Developers to integrate your AI components into orchestration engines, configurable web interfaces, and end-to-end automated workflows. You will not work alone — every AI solution at Greystar is a team sport.

Crucially, you will not be the developer who jumps to code or embeds an LLM the moment a request lands. You will be the developer who first asks can we improve the underlying business process, then is there a simpler technical answer, and only then what is the right AI design here. You will care as much about logging, configurability, reusability, and end-user flexibility as you do about model accuracy. And you will deliver, faster than expected, on every commitment.

What You Will do

• Partner with the Business Analyst from the earliest stages of every engagement to understand the business problem, the data flow, the upstream triggers, and the downstream consequences — before writing a single line of code.
• Work with the AI Architect to align on the solution design, the reusable framework to be extended, the cloud-native architecture, and the LLM/NLP/OCR approach for each engagement.
• Develop AI-powered components for solutions across Finance & Accounting use cases — invoice and document extraction, contract abstraction, anomaly detection, classification, summarization, conversational interfaces, RAG-based knowledge retrieval and many more.
• Build cloud-native solutions on Microsoft Azure — leveraging Azure OpenAI, Azure AI Services (Document Intelligence, Vision, Language), Azure Functions, Azure Storage, Azure SQL, Service Bus, and Azure API Management — ensuring every solution is scalable and secure by design. You will use Claude and Microsoft 365 ecosystem also depending on the business requirements.
• Implement enterprise-grade logging across every system and user activity, so that every action in production is traceable, observable, and auditable.
• Build solutions that are highly configurable — no static or hard-coded values — with parameters such as processing volume, run schedule, batch size, model temperature, confidence thresholds, and human-in-the-loop split exposed for end-user control.
• Develop and continuously improve a library of reusable LLM components — prompt templates, RAG patterns, document chunking, evaluation harnesses, guardrails, and output parsers — so that no new AI module is written from scratch when an existing one can be extended.
• Collaborate with Automation Developers to ensure every AI solution plugs cleanly into the COE's orchestration engine, the configurable web interface that shows real-time progress to end users, and the broader automation flow.
• Treat every business problem as an opportunity to ask whether the underlying process can be improved before reaching for an AI hammer — and raise this constructively with the Business Analyst, AI Architect, and stakeholders when you see it.
• Implement responsible AI practices — prompt injection defence, output validation, hallucination grounding, PII handling, content filters, and access controls — because we work with sensitive financial and contractual data.
• Build evaluation harnesses and test sets for every AI component you ship — measuring accuracy, precision, recall, latency, cost, and drift over time.
• Write clean, well-documented, and well-tested code in Python and / or .NET / C#, with appropriate unit tests, integration tests, and CI/CD pipelines on Azure DevOps.
• Support UAT and production cutover alongside the Business Analyst and Automation Developers, debug production issues quickly, and contribute to post-go-live hypercare.
• Deliver against committed timelines — and where possible, ahead of them — because the COE's credibility with the global business is built one delivery at a time.
• Stay current on the rapidly evolving AI landscape — new models, new patterns, new tools — and proactively bring ideas to the team that improve our delivery speed, accuracy, or cost.

Who You Are

• Genuinely inquisitive. You ask questions before you write code. You want to understand the business problem, the data flow, and the user journey — not just the ticket description.
• Disciplined enough not to jump to a solution. You resist the temptation to start coding the moment a request arrives, and you are comfortable saying let me understand this first.
• A process-improver at heart. You see your job as making the business better — not just embedding an LLM into whatever workflow lands on your desk.
• Ultra passionate about delivery speed. You take pride in delivering on time, and quietly proud when you deliver below time — without compromising quality.
• Highly collaborative. You know that an AI component without orchestration, a web interface, logging, and configurability is half a product — and you genuinely enjoy working with the Automation Developers, Architect, and BA to ship the whole thing.
• Hungry to learn. AI is changing every quarter, and you do not complain about having to learn new tools, models, and patterns alongside delivery commitments — you find it energising.
• Detail-oriented in production thinking. You instinctively think about logs, retries, exceptions, fallbacks, and edge cases — because you know that AI solutions live or die in the long tail.
• Calm under pressure, honest about progress, and happy to ask for help when stuck.

What You Have

• Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, Data Science, or a related discipline from an accredited university.
• 6 to 10 years of overall software development experience, with a meaningful portion of the last 2–3 years dedicated to building production AI / GenAI / NLP / OCR solutions.
• Hands-on production experience with Large Language Model APIs — Anthropic Claude, OpenAI / Azure OpenAI, or equivalent — including prompt engineering, structured output extraction, function / tool calling, and streaming.
• Working experience with Retrieval-Augmented Generation (RAG) patterns — embeddings, vector stores (Azure AI Search, Pinecone, FAISS, or equivalent), chunking strategies, hybrid search, and retrieval evaluation.
• Hands-on experience with at least one OCR / Document Intelligence platform — Azure AI Document Intelligence (Form Recognizer), AWS Textract, Google Document AI, or equivalent — including post-OCR LLM-based extraction and validation.
• Strong programming skills in Python and / or .NET / C#, with practical experience writing production-grade, maintainable, well-tested code.
• Hands-on experience building cloud-native applications on Microsoft Azure — Azure Functions, AKS, Azure Storage, Azure SQL, Cosmos DB, Service Bus, Event Grid, Azure API Management — with a strong appreciation for scalability and security.
• Working understanding of microservice architecture, RESTful APIs, asynchronous messaging, and event-driven patterns.
• Demonstrable experience implementing structured logging, observability, and monitoring — Azure Monitor, Application Insights, Log Analytics, or equivalent — in production systems.
• Experience designing and implementing configurable systems — externalized configuration, feature flags, parameterised workflows — so end users can adjust operational metrics without a developer touching code.
• Experience integrating AI components into orchestration engines and workflow platforms (e.g. Azure Logic Apps, Durable Functions, Power Automate, Camunda, Airflow, or RPA platforms such as UiPath / Power Automate Desktop / Blue Prism).
• Comfort building or contributing to web interfaces (React, Blazor, or equivalent) that display progress, exceptions, and end-user controls in real time. This is not a mandatory requirement but will be an advantage.
• Strong understanding of responsible AI — prompt injection, hallucination, grounding, PII handling, evaluation, and guardrails — with a track record of applying these in production code.
• Experience with CI/CD on Azure DevOps or GitHub Actions, Git-based workflows, and basic DevOps / MLOps / LLMOps practices.
• Working knowledge of Agile / Scrum delivery.
• Exposure to or genuine interest in Finance & Accounting business processes is a strong plus. Prior experience in real estate, fintech, banking, shared services, or finance transformation is welcome — not mandatory.
• Strong written and verbal English communication skills — because you will collaborate with the Business Analyst, the Architect, the Automation Developers, and occasionally with global business stakeholders.
• Certifications such as Microsoft Certified: Azure AI Engineer Associate, Azure Developer Associate, or equivalent are desirable but not mandatory.