Senior Artificial Intelligence/Machine Learning Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Artificial Intelligence/Machine Learning Engineer based in India. The Senior Artificial Intelligence/Machine Learning Engineer will design and deliver production-grade AI systems that transform complex, unstructured documents into accurate, structured data.

This hands-on engineering role focuses on agentic AI, document intelligence, large language models (LLMs), and retrieval-augmented generation (RAG) within scalable AWS environments. You will build intelligent extraction agents, resilient document ingestion pipelines, and confidence-based routing systems that balance automation with human oversight.

The role emphasizes reliable outputs, secure domain-specific knowledge, measurable model performance, and responsible AI governance. You will also optimize inference costs and latency while developing evaluation frameworks and feedback loops that continuously improve system quality. Working within a collaborative, cross-functional engineering team, you will mentor other engineers and help establish reusable technical patterns for dependable, high-impact AI solutions.

Accountabilities Agent development

Design, build, test, and maintain production-grade extraction agents that convert unstructured document content into structured target schemas while supporting multiple schema variants.

Document ingestion engineering

Develop robust ingestion pipelines capable of processing degraded scans, multi-generation photocopies, mixed image-and-text PDFs, and documents with inconsistent layouts.

Confidence modeling and routing

Build field-level confidence models and routing mechanisms that allow high-confidence outputs to proceed automatically while directing uncertain results to human review.

Knowledge engineering and RAG

Build and configure retrieval-augmented generation knowledge bases that provide agents with secure, domain-specific context, improve grounding, and reduce hallucinations when incorrect values could have significant downstream consequences.

Human-in-the-loop governance

Implement review and escalation workflows, confidence thresholds, and output controls. Ensure reviewer corrections are captured and used as feedback signals to improve future performance.

Change detection and reprocessing

Develop comparison workflows that evaluate new extraction results against existing records and highlight meaningful changes.

Model and pipeline optimization

Configure model selection, prompt and context strategies, token usage, inference costs, and latency across the end-to-end AI pipeline.

Evaluation and monitoring

Establish mechanisms for assessing extraction accuracy, monitoring production performance, identifying quality issues, and improving system reliability over time.

Technical ownership

Own the AI system lifecycle from document ingestion and inference through evaluation, monitoring, governance, and feedback-driven improvement.

Engineering mentorship

Provide technical guidance to mid-level AI engineers and establish standardized patterns for extraction, confidence estimation, agent orchestration, and system design.

Cross-functional collaboration

Work with relevant engineering and product stakeholders to translate business needs into dependable AI capabilities and scalable technical solutions.

Requirements

Deep experience in AWS architecture, including hands-on work with Amazon Bedrock in production environments. Strong expertise in model selection, prompt engineering, context engineering, model evaluation, and optimizing inference costs and latency. Substantial experience with document AI and optical character recognition (OCR), including processing degraded inputs using Amazon Textract or equivalent technologies. Strong understanding of layout-aware extraction and the challenges of transforming OCR output into reliable, schema-compliant structured data.

Demonstrated experience extracting structured information from long, unstructured documents into fixed schemas, including field-level confidence estimation and calibration. Proven ability to build agentic AI systems involving multi-step reasoning, tool-use protocols, and state management for autonomous agents. Production experience building RAG or LLM pipelines, including document chunking, retrieval strategies, contextual grounding, and knowledge integration.

Experience designing human-in-the-loop systems that route uncertain model outputs for review rather than relying on unverified automated decisions. Ability to incorporate reviewer corrections into feedback loops that support continuous model and system improvement. Strong end-to-end ownership, including responsibility for ingestion, inference, evaluation frameworks, monitoring, and production feedback mechanisms.

Desirable experience

Familiarity with selective prediction and confidence calibration techniques, including determining when a model should abstain because it lacks sufficient confidence.

Desirable experience

Building programmatic evaluation frameworks and testing against curated reference datasets to measure field-level accuracy, review-queue precision and recall, and model drift.

Desirable experience

Working with proprietary domain-specific datasets and developing data governance practices for specialized knowledge sources. Strong problem-solving, communication, and collaboration skills, with the ability to guide other engineers and establish effective engineering practices.

Benefits

Work arrangement: Remote position in India.

Collaborative environment

Join a supportive engineering community with an open-door culture and opportunities to work alongside experienced professionals.

Career development

Contribute to large-scale projects with global impact while expanding expertise in AI, machine learning, and advanced engineering technologies.

Learning opportunities

Access internal meetups, conferences, workshops, language courses, Udemy learning resources, and company-paid certifications.

Internal mobility

Explore opportunities across different business domains and projects to broaden experience and develop new technical skills.

Healthcare and well-being

Company-paid medical insurance and mental health support.

Additional support

Access to financial and legal consultations.

Compensation details

A specific salary range, bonus structure, paid leave policy, and other compensation details are not provided in the original job description.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether?

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