Senior AI Engineer

Location: Hyderabad, Telangana, India

Department: 204 - Product Development

Role - Senior AI Engineer

About the Role

GHX is building an LLM-powered document understanding platform — classification, structured data extraction, and intelligent orchestration at scale. This is a broad AI engineering role where you will own the full lifecycle from problem framing through to production quality.

You will start where the highest leverage is: designing prompts that behave like specifications and building the evaluation infrastructure that tells us whether the system actually works. Over time, the scope expands into agent orchestration, system architecture, and the migration of our existing rule-based pipeline to LLM-based equivalents.

Solid software engineering experience is a pre-requisite. You will design and build production services, own architectural decisions, and write code that others maintain. AI fluency amplifies good engineering – it does not replace it.

How the Role Evolves

Core Responsibilities

Prompt Engineering

• Design classification and extraction prompts for diverse document types
• Write prompts that function as formal specifications — unambiguous and edge-case-aware
• Build and iterate few-shot, chain-of-thought, and structured output templates
• Own the prompt library, versioning, and rollback strategy

LLM Output Evaluation

• Define and curate ground truth datasets for classification and extraction tasks
• Build automated evaluation pipelines tracking precision, recall, and field-level accuracy
• Validate outputs against intent — catch what is technically correct but conceptually wrong

AI Agent Orchestration

• Design multi-agent pipelines for document processing workflows
• Integrate MCP servers and tool-use patterns for external service access

Software Engineering

• Build production-grade APIs and services around LLM capabilities
• Apply Clean Architecture or equivalent — design for testability and maintainability
• Write well-structured Python; own technical debt decisions consciously
• Contribute to CI/CD, observability, and deployment pipelines

Stakeholder Interface

• Serve as technical contact between business teams and the AI engineering layer
• Communicate system constraints in product language; translate product needs into system boundaries
• Surface quality metrics and model behaviour to non-technical stakeholders

Skills & Experience

Required

• Prompt engineering (advanced) & LLM evaluation frameworks
• Ground truth dataset design
• LLM APIs — OpenAI, Anthropic, Azure AI
• 8+ years overall software engineering experience preferably in Python & AWS
• AI agent orchestration
• Clean Architecture / DDD
• REST API design
• Git, CI/CD, containerisation

Nice to Have

• Document understanding / OCR tooling like Textract
• Experience working with AWS services (EC2, SQS, S3, Lambda functions, ECS)
• LangChain / LlamaIndex
• NLP / text classification

What Good Looks Like

• Prompt as specification. Treats prompts like specifications — precise, contract-like, with edge cases considered upfront rather than patched later.
• Verification instinct. Reads AI output critically. Catches the subtly wrong answer before it becomes an expensive data quality incident downstream.
• Evaluation-first mindset. Doesn't trust a prompt in production without a test suite. Builds measurement infrastructure as part of the feature, not after.
• Architectural thinking. Understands how systems should be structured before implementation begins. Knows the difference between technically correct and conceptually right.
• Pattern fluency. Knows Clean Architecture, DDD, or equivalent — not the syntax, the reasoning behind the design choices.
• Communication range. Can speak to a non-technical ops manager and a distributed system at the same level of precision.