Senior AI Engineer

Senior AI Engineer

Location: Hyderabad, Telangana, India

About the Role

GHX is building a cutting-edge LLM-powered document understanding platform focused on classification, structured data extraction, and intelligent orchestration at scale.

This is a high-impact AI engineering role where you will own the full lifecycle—from problem framing to production deployment. Initially, you will focus on prompt engineering and evaluation systems, building the quality foundation for AI performance. Over time, the role expands into agent orchestration, system architecture, and migration of rule-based systems to LLM-driven pipelines.

A strong foundation in software engineering (5+ years) is essential. This role demands engineering rigor across both traditional system design and AI system behavior.

Role Evolution

Now (0–6 months): Prompt Engineering & Evaluation

Design and refine prompts for classification and data extraction

Build ground truth datasets and evaluation pipelines

Establish accuracy benchmarks and quality baselines

Mid-Term (6–12 months): Agent Orchestration & Pipeline Design

Develop multi-agent document processing pipelines

Integrate MCP-based tools and external service interfaces

Orchestrate parallel AI workflows

Long-Term (12+ months): Platform Architecture & Ownership

Own the end-to-end document intelligence architecture

Lead migration from rule-based to LLM-powered systems

Define engineering standards and best practices

Core Responsibilities

1. Prompt Engineering

Design prompts for diverse document classification and extraction tasks

Treat prompts as formal specifications (precise, structured, and edge-case-aware)

Develop few-shot, chain-of-thought, and structured output templates

Manage prompt lifecycle: versioning, testing, and rollback

2. LLM Output Evaluation

Create and maintain ground truth datasets

Build automated evaluation pipelines (precision, recall, field-level accuracy)

Identify and resolve conceptually incorrect outputs despite surface correctness

3. AI Agent Orchestration

Design multi-agent workflows for document processing

Implement tool-use patterns and integrate MCP servers

Optimize orchestration for scale and efficiency

4. Software Engineering

Develop production-grade APIs and backend services

Apply Clean Architecture / DDD principles

Write maintainable, testable Python code

Contribute to CI/CD, deployment, and observability systems

5. Stakeholder Collaboration

Act as a bridge between business stakeholders and AI systems

Translate product requirements into technical architectures

Communicate system behavior, limitations, and quality metrics clearly

Required Skills & Experience

5+ years of software engineering experience (Python preferred)

Advanced prompt engineering and LLM evaluation expertise

Experience with ground truth dataset design

Hands-on experience with LLM APIs (OpenAI, Anthropic, Azure AI)

AI agent orchestration experience

Strong understanding of Clean Architecture / DDD principles

REST API development

Version control (Git), CI/CD practices, containerization

Experience with AWS or similar cloud platforms

Nice to Have

Document understanding / OCR tools (e.g., AWS Textract)

Experience with AWS services (EC2, S3, SQS, Lambda, ECS)

Familiarity with LangChain / LlamaIndex

Background in NLP or text classification systems