Senior Artificial Intelligence/Machine Learning Engineer

Jobgether; India; A distancia; INR 900.000 - 1.500.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Remote position in India

Collaborative engineering culture

Career development and learning

Descripción de la vacante

Jobgether, on behalf of a partner in India, seeks a Senior AI/ML Engineer to design production‑grade AI systems for document intelligence, using LLMs, RAG, and AWS Bedrock.

You will build extraction agents, robust ingestion pipelines, confidence models, and governance workflows, while mentoring engineers and driving end‑to‑end delivery from ingestion to production monitoring.

Formación

• Deep experience in AWS architecture and production use of Bedrock.
• Strong skills in model selection, prompt engineering, and evaluation.
• Experience with document AI and OCR, incl. Textract or equivalent.
• Understanding of layout-aware extraction and schema‑compliant data.
• Proven ability to build agentic AI systems with multi-step reasoning.
• Experience with RAG/LLM pipelines and human-in-the-loop governance.

Responsabilidades

• Design, build, test, and maintain production-grade extraction agents converting unstructured content to structured schemas for multiple variants.
• Develop ingestion pipelines handling degraded scans and complex documents.
• Create confidence models and routing to automate high-confidence outputs and route uncertain results to humans.
• Build knowledge bases for secure, domain-specific grounding and reduce hallucinations.
• Implement governance, review workflows, and feedback signals to improve performance.
• Develop change-detection, reprocessing, and evaluation frameworks to monitor quality over time.
• Own the AI system lifecycle from ingestion to production feedback.
• Mentor mid-level engineers and establish reusable patterns for extraction and system design.
• Collaborate with product and engineering to translate business needs into scalable AI capabilities.

Conocimientos

AWS architecture

Model selection

Prompt engineering

Context engineering

Model evaluation

Inference cost

Latency optimization

Document AI

OCR

Layout extraction

Schema mapping

Confidence estimation

Calibration

RAG pipelines

LLM pipelines

Human-in-the-loop

Governance

End-to-end ownership

Mentorship

Cross-functional

Herramientas

Amazon Bedrock

Amazon Textract

Descripción del empleo

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.