Lead Applied AI Engineer

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.