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
• Role Category: AI Engineer
• Skill Category: AI and Automation

Role Summary

• Architect, build, deploy, and scale production-grade AI solutions.

• Integrate:

• Generative AI
• AI agents

• Modern enterprise platforms

• Support large-scale business operations.

• Ensure strong standards for:

• Security
• Reliability
• Governance

• Responsible AI

• Define enterprise AI technical standards and engineering best practices.
• Lead enterprise AI adoption.
• Mentor engineering teams.
• Work across AI innovation, enterprise architecture, platform engineering, and responsible AI governance.

Key Responsibilities

AI Solution Architecture

• Architect end-to-end AI systems.
• Design advanced RAG (Retrieval-Augmented Generation) pipelines.
• Develop multi-stage retrieval and re-ranking architectures.
• Design agent orchestration frameworks for multiple specialized agents.
• Integrate multiple AI models based on their specific strengths.

• Build solutions with:

• Modularity
• Extensibility
• Scalability
• Operational excellence

AI Engineering Standards & Optimization

• Define enterprise standards for:

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

• Evaluation frameworks

• Establish performance optimization strategies covering:

• Model selection
• Caching
• Resource utilization
• Cost optimization

Production Deployment & Reliability

• Lead deployment of AI solutions into production.

• Implement:

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

• Incident response procedures

• Ensure AI services meet enterprise service-level objectives and reliability expectations.

Data & Retrieval Architecture

• Design scalable data ingestion frameworks for:

• Structured data
• Unstructured documents

• Real-time event streams

• Develop:

• Vector database architectures
• Hybrid search capabilities
• Data preprocessing pipelines

• Data quality monitoring frameworks

• Implement data cleansing, enrichment, and governance processes.
• Ensure high-quality 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.

• Work with:

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

• Networking infrastructure

• Define requirements for enterprise AI platforms 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 meet enterprise governance and compliance requirements.

• Maintain documentation covering:

• System behavior
• Decision logic

• Evaluation methodologies

• Apply responsible AI principles:

• Fairness
• Transparency
• Accountability

• Bias mitigation

• Support applicable regulatory and industry requirements.

Required Qualifications

• 7+ years of software engineering experience with a strong focus on AI/ML engineering.
• 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.

• Experience with:

• Advanced RAG architectures
• Multi-hop retrieval
• Reasoning systems
• Agent orchestration frameworks
• Tool-using AI agents
• Memory-enab…