T
Lead Applied AI Engineer
Talent Software Services
3 hours ago
Full-time
On-site
Lead Applied AI Engineer
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 Enterprise Solution Delivery
Lead complex AI initiatives across multiple cross-functional teams. Translate business objectives into Technical solutions, AI architectures, Delivery roadmaps Drive AI initiatives from Concept, Architecture, Development, Production deployment, Optimization Technical Skills
Python, FastAPI, React, Distributed systems, Vector databases, Embedding models, LLM APIs, Agent orchestration frameworks, Cloud-native architectures, AI platform engineering, Production AI deployment AI Engineering Best Practices
Establish enterprise standards for Prompt engineering, Version control, Testing, AI evaluation, Model observability, Cost tracking, Performance tracking, Benchmarking, Data-driven optimization Responsible AI & Governance
Strong understanding of Responsible AI, Model governance, Risk management, Model validation, Change management, Production monitoring, Deployment practices in regulated environments Preferred Qualifications
Technical leadership across organizational boundaries. Strong mentoring and coaching skills. Ability to collaborate with Product Management, Data Science, Engineering, Security, Compliance, Architecture, Business stakeholders Experience in regulated industries preferred, including Healthcare, Life Sciences, Insurance Primary Skills for TAG Search
Must Have
Generative AI Agentic AI RAG Architecture AI Agents Multi-Agent Systems Python FastAPI Vector Databases LLM Integration AI Platform Engineering Production AI Deployment AI Evaluation Frameworks Prompt Engineering Observability & Monitoring Enterprise Architecture Strongly Preferred
React Cloud AI Platforms Azure OpenAI / Azure OpenAI Healthcare Domain Experience Responsible AI AI Governance Distributed Systems Engineering Keywords
AI Engineer Applied AI Engineer Lead AI Engineer Generative AI Agentic AI AI Agents RAG LLM LLM Integration Multi-Agent Systems Python FastAPI React Vector Database AI Platform AI Architecture AI Evaluation Prompt Engineering AI Governance Responsible AI Cloud AI Azure OpenAI Distributed Systems AI and Automation
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 Enterprise Solution Delivery
Lead complex AI initiatives across multiple cross-functional teams. Translate business objectives into Technical solutions, AI architectures, Delivery roadmaps Drive AI initiatives from Concept, Architecture, Development, Production deployment, Optimization Technical Skills
Python, FastAPI, React, Distributed systems, Vector databases, Embedding models, LLM APIs, Agent orchestration frameworks, Cloud-native architectures, AI platform engineering, Production AI deployment AI Engineering Best Practices
Establish enterprise standards for Prompt engineering, Version control, Testing, AI evaluation, Model observability, Cost tracking, Performance tracking, Benchmarking, Data-driven optimization Responsible AI & Governance
Strong understanding of Responsible AI, Model governance, Risk management, Model validation, Change management, Production monitoring, Deployment practices in regulated environments Preferred Qualifications
Technical leadership across organizational boundaries. Strong mentoring and coaching skills. Ability to collaborate with Product Management, Data Science, Engineering, Security, Compliance, Architecture, Business stakeholders Experience in regulated industries preferred, including Healthcare, Life Sciences, Insurance Primary Skills for TAG Search
Must Have
Generative AI Agentic AI RAG Architecture AI Agents Multi-Agent Systems Python FastAPI Vector Databases LLM Integration AI Platform Engineering Production AI Deployment AI Evaluation Frameworks Prompt Engineering Observability & Monitoring Enterprise Architecture Strongly Preferred
React Cloud AI Platforms Azure OpenAI / Azure OpenAI Healthcare Domain Experience Responsible AI AI Governance Distributed Systems Engineering Keywords
AI Engineer Applied AI Engineer Lead AI Engineer Generative AI Agentic AI AI Agents RAG LLM LLM Integration Multi-Agent Systems Python FastAPI React Vector Database AI Platform AI Architecture AI Evaluation Prompt Engineering AI Governance Responsible AI Cloud AI Azure OpenAI Distributed Systems AI and Automation