Lead Applied AI Engineer

Job Details

• Job Title: Lead Applied AI Engineer
• Location: New York, NY / Louisville, KY
• Duration: 6 months

• Preferred Work Location:

• 1700 Broadway, Suite 3400, New York, NY 10019

• Louisville, KY

• Experience Required: 8-10 years
• Primary Skill Category: AI and Automation

Role Summary

• Architect, build, deploy, and scale advanced enterprise AI solutions.

• Integrate:

• Generative AI
• AI agents

• Modern enterprise platforms

• Deliver production-grade AI systems supporting large-scale business operations.

• Ensure high standards for:

• Security
• Reliability
• Governance
• Responsible AI

• Scalability

• Define enterprise AI engineering standards and best practices.
• Lead enterprise AI adoption.
• Mentor engineering teams.

• Work across:

• AI innovation
• Enterprise architecture
• Platform engineering
• Responsible AI governance

AI Solution Architecture

• Architect end-to-end AI systems, including:

• Advanced RAG pipelines
• Multi-stage retrieval and re-ranking
• Agent orchestration frameworks
• Multi-agent architectures

• Multi-model AI integrations

• Design modular, extensible, scalable, and operationally efficient solutions.
• Build architectures that can adapt to evolving business requirements.

AI Engineering Standards & Optimization

• Define enterprise standards for:

• Prompt engineering
• Prompt templates
• Prompt versioning
• Testing methodologies

• AI evaluation frameworks

• Establish optimization strategies for:

• Model selection
• Caching
• Resource utilization
• Cost optimization
• Performance

Production Deployment & Reliability

• Lead production deployment of AI solutions.

• Implement:

• Observability
• Logging
• Distributed tracing
• Reliability engineering
• Graceful degradation
• Circuit breakers
• Real-time monitoring dashboards
• Automated alerting

• Incident response procedures

• Ensure AI services meet enterprise SLOs and reliability requirements.

Data & Retrieval Architecture

• Design scalable data ingestion frameworks for:

• Structured data
• Unstructured documents

• Real-time event streams

• Develop:

• Vector database architectures
• Hybrid search
• Data preprocessing pipelines

• Data quality monitoring

• Implement data cleansing, enrichment, and governance processes.
• Ensure high-quality data inputs for AI systems.

AI Evaluation & Continuous Improvement

• Establish quantitative AI evaluation frameworks.

• Implement:

• A/B testing
• Performance benchmarking
• User feedback analysis

• Telemetry-based optimization

• Continuously improve:

• Prompts
• Retrieval strategies
• Agent workflows
• Model configurations

Platform & Infrastructure Collaboration

• Partner with platform and infrastructure teams to support AI workloads.

• Define requirements for:

• GPU infrastructure
• Model-serving platforms
• Feature stores
• Scalable data storage

• Networking infrastructure

• Define enterprise AI platform capabilities and integration patterns.

Technical Leadership & Mentoring

• Mentor engineers through:

• Architecture reviews
• Design guidance
• Code reviews

• Career development

• Promote engineering excellence through:

• Best-practice documentation
• Technical training

• Communities of practice

• Foster responsible and ethical AI development.

Responsible AI & Compliance

• Ensure AI solutions comply with enterprise governance and regulatory requirements.

• Document:

• System behavior
• Decision logic

• Evaluation methodologies

• Apply responsible AI principles, including:

• Fairness
• Transparency
• Accountability

• Bias mitigation

• Support compliance with applicable regulatory and industry standards.

Required Qualifications

• 7+ years of software engineering experience with strong AI/ML focus.
• Proven experience building and operating distributed systems at scale.
• Demonstrated success delivering AI-driven business outcomes.
• Experience leading large and complex technical initiatives.

• Bachelor’s degree in:

• Computer Science
• Engineering
• Data Science

• Related discipline

• Equivalent practical experience may be considered.

Generative AI Expertise

• Deep experience designing and deploying production-grade Generative AI solutions.

• Strong experience with:

• Advanced RAG architectures
• Multi-hop retrieval
• Reasoning systems
• Agent orchestration
• Tool-using AI agents
• Memory-enabled AI systems
• Multi-model AI architectures
• Conversational AI platforms

Ente…