Senior Artificial Intelligence/Machine Learning Engineer

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.