Lead Applied AI Engineer

Key Responsibilities

• AI Solution Architecture
• Architect comprehensive end-to-end AI systems including Advanced RAG (Retrieval-Augmented Generation) pipelines, multi-stage retrieval and re-ranking architectures, agent orchestration frameworks, and multi-model AI integrations.
• Define prompt engineering, prompt templates, and versioning, establish testing methodologies, and develop evaluation frameworks.
• Establish performance optimization strategies covering model selection criteria, caching patterns, resource utilization, cost optimization, and other relevant areas.
• Lead deployment of AI solutions into production environments with comprehensive observability, logging and tracing, reliability engineering practices, graceful degradation mechanisms, circuit breaker implementation, real-time monitoring dashboards, automated alerting, incident response procedures, and ensure meeting stringent service-level objectives and enterprise reliability expectations.
• Design scalable data ingestion frameworks that process structured data sources, unstructured documents, and real-time event streams, develop vector database architectures, hybrid search capabilities, data preprocessing pipelines, data quality monitoring frameworks, and ensure high-quality inputs for AI systems through cleansing, enrichment, and governance processes.
• Establish quantitative evaluation frameworks for AI systems, implement A/B testing capabilities, performance benchmarking, user feedback analysis, telemetry-based optimization, and drive continuous improvements across prompts, retrieval strategies, agent workflows, and model configurations.
• AI Engineering Standards & Optimization
• Define enterprise standards for prompt engineering, prompt templates and versioning, testing methodologies, and evaluation frameworks.
• Establish performance optimization strategies covering model selection criteria, caching patterns, resource utilization, cost optimization, and other relevant areas.
• Production Deployment & Reliability
• Ensure AI services meet stringent service-level objectives and enterprise reliability expectations.
• Implement comprehensive observability, logging and tracing, reliability engineering practices, graceful degradation mechanisms, circuit breaker implementation, real-time monitoring dashboards, automated alerting, incident response procedures, and other relevant measures.
• Data & Retrieval Architecture
• Develop scalable data ingestion frameworks that process structured data sources, unstructured documents, and real-time event streams.
• Design vector database architectures, hybrid search capabilities, data preprocessing pipelines, and data quality monitoring frameworks.
• Ensure high-quality inputs for AI systems through cleansing, enrichment, and governance processes.
• Ai Evaluation & Continuous Improvement
• Establish quantitative evaluation frameworks for AI systems, implement A/B testing capabilities, performance benchmarking, user feedback analysis, and telemetry-based optimization.
• Drive continuous improvements across prompts, retrieval strategies, agent workflows, and model configurations.
• Technical Leadership & Mentoring
• Mentor engineers through architecture reviews, design guidance, code reviews, career development support, and promote engineering excellence through best-practice documentation, technical training, and communities of practice.
• Foster a culture of responsible and ethical AI development.

Qualifications

• Bachelor of Computer Science