Senior AI / ML Engineer – Agentic Document Intelligence
Experience: 6–9 Years Location: Chennai, Pune or Remote – India Budget: Up to ₹2.2 LPM Payroll: Haparz
About the Role
We are looking for a Senior AI / ML Engineer to design and develop a production-ready document intelligence platform on AWS. The platform will process complex PDFs, including low-quality scans, scanned photocopies, mixed image-text documents, and inconsistent layouts, and convert them into structured records that conform to predefined business schemas.
The solution will use Amazon Bedrock, OCR, LLM-powered extraction agents, RAG, field-level confidence scoring, and human-in-the-loop (HITL) review to ensure reliable and traceable document processing.
You will own the engineering lifecycle from document ingestion and extraction to validation, automated review routing, evaluation, monitoring, and continuous improvement.
Key Responsibilities
- Design and develop production-grade AI agents that extract information from unstructured documents and map it to predefined schemas, supporting multiple document types and schema variations.
- Build robust document ingestion and preprocessing pipelines for low-resolution scans, multi-generation photocopies, mixed PDF formats, and inconsistent document layouts.
- Integrate OCR solutions such as Amazon Textract or equivalent technologies to improve text recognition, layout understanding, and extraction accuracy.
- Implement field-level confidence scoring and configurable thresholds to determine which records can be processed automatically and which require human review.
- Develop and maintain RAG pipelines and knowledge bases to provide domain-specific context, improve grounding, and minimize hallucinations.
- Implement agentic workflows involving multi-step reasoning, tool invocation, state management, validation, and exception handling.
- Build human-in-the-loop review and escalation mechanisms, using reviewer corrections as feedback for improving extraction quality.
- Develop comparison and reprocessing workflows to identify meaningful differences between newly extracted data and previously stored records.
- Optimize model selection, prompts, context windows, token consumption, inference costs, latency, and throughput.
- Establish evaluation frameworks using golden datasets and field-level accuracy metrics to measure extraction quality, review-routing performance, and model drift.
- Implement monitoring, security controls, data governance, and access restrictions for sensitive documents and extracted information.
- Collaborate with AI Architects and Data Engineers to establish reusable architecture patterns and mentor mid-level engineers.
Required Experience
- 6–9 years of relevant software engineering, AI/ML, NLP, or applied AI experience, with substantial hands-on ownership of production AI systems.
- Strong AWS architecture knowledge and practical experience with Amazon Bedrock, including model selection, prompt engineering, evaluation, and production optimization.
- Experience with OCR technologies such as Amazon Textract or equivalent, including degraded scans, layout-aware processing, and document preprocessing.
- Demonstrated expertise in extracting structured data from long, unstructured documents into predefined schemas.
- Experience developing LLM agents, multi-step workflows, tool-use patterns, state management, and orchestration.
- Hands-on experience implementing production RAG pipelines, including document chunking, retrieval, grounding, and knowledge-base configuration.
- Understanding of field-level confidence estimation, validation rules, automated routing, and human-in-the-loop review processes.
- Experience owning production pipelines end to end, including evaluation, monitoring, exception handling, and feedback-driven improvements.
- Strong problem-solving skills and the ability to make engineering trade-offs involving accuracy, cost, latency, security, and scalability.
Good to Have
- Experience with selective prediction, confidence calibration, and abstention strategies for uncertain model outputs.
- Experience building programmatic LLM evaluation frameworks and testing against golden datasets.
- Familiarity with precision, recall, field-level accuracy, and drift monitoring for document extraction systems.
- Experience working with proprietary domain-specific corpora and implementing data governance controls.
- Experience with automated regression testing, CI/CD, observability, and production incident troubleshooting.
Work Location: Remote
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