Job Title: Lead Applied AI Engineer
Location: Remote
Employment Type: Full-time
Job Description:
Lead Applied AI Engineer
Overview
We are seeking an accomplished Lead Applied AI Engineer to architect and deliver advanced AI systems that seamlessly integrate Generative AI capabilities, AI agents, and modern enterprise platforms. This role is responsible for designing, building, deploying, and scaling production-grade AI solutions that support large-scale business operations while maintaining high standards of security, reliability, governance, and responsible AI practices. The Lead Applied AI Engineer will define technical standards, lead enterprise AI adoption, establish engineering best practices, and mentor engineering teams at the intersection of AI innovation, enterprise architecture, platform engineering, and responsible AI governance.
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 coordinating multiple specialized agents
Multi-model AI integrations leveraging model-specific strengths
Design solutions with modularity, extensibility, scalability, and operational excellence to support evolving business requirements.
AI Engineering Standards & Optimization
Define enterprise standards for:
Prompt engineering, templates, and versioning
Testing methodologies and evaluation frameworks
Establish performance optimization strategies covering:
Model selection criteria
Caching patterns
Resource utilization and cost optimization
Production Deployment & Reliability
Lead the deployment of AI solutions into production environments with:
Comprehensive observability, logging, and tracing
Reliability engineering practices, graceful degradation mechanisms, and circuit breakers
Real-time monitoring dashboards, automated alerting, and incident response procedures
Ensure AI services meet stringent service-level objectives (SLOs) and enterprise reliability expectations.
Data & Retrieval Architecture
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, 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.
Platform & Infrastructure Collaboration
Partner with platform and infrastructure teams to ensure readiness for AI workloads (GPU infrastructure, model serving platforms, feature stores, scalable data storage, and networking).
Define requirements for enterprise AI platform capabilities and integration patterns.
Technical Leadership & Mentoring
Mentor engineers through architecture reviews, design guidance, code reviews, and career development support.
Promote engineering excellence through best-practice documentation, technical training, and communities of practice.
Foster a culture of responsible and ethical AI development.
Responsible AI & Compliance
Ensure AI solutions adhere to enterprise governance and compliance requirements.
Maintain comprehensive documentation of system behavior, decision logic, and evaluation methodologies.
Apply responsible AI principles including fairness, transparency, accountability, and bias mitigation while ensuring compliance with applicable regulatory and industry standards.
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