T
Artificial Intelligence Engineer
Tiger Advisory
3 hours ago
Full-time
On-site
New York City, New York, United States
Principal AI Engineer
Location:
New York City (Hybrid: 2-3 days on-site) Employment Type:
Full Time Employment Turing is hiring a Staff/Principal AI Engineer to lead enterprise-scale agentic AI implementations for Fortune 500 clients. This is a hands-on engineering role focused on designing and shipping autonomous, tool-calling AI systems — agents that reason over enterprise context, invoke real systems through secure interfaces, and operate reliably at scale under strict latency, cost, and governance constraints. You will own these systems end to end: the data pipelines feeding them, the backend services around them, the agent orchestration layer, the evaluation harness that keeps them honest, and the cloud infrastructure they run on. We are looking for engineers with genuine software engineering and data science depth who have taken agentic systems all the way to production.
Lead the architecture and implementation of tool-calling agents that combine retrieval, structured reasoning, and secure action execution with least-privilege access. Productionize LLM applications:
Build retrieval pipelines, prompt synthesis, response validation, and self-correction loops, backed by rigorous evaluation. Own the full stack:
Deliver the data pipelines, backend services, distributed compute, and orchestration layer that agentic systems depend on — not only the model invocation. Build validator models, adversarial test suites, and policy checks; Drive measurable improvements in token efficiency, response time, and unit economics against defined SLOs. Build, maintain, and review high-quality Python and SQL, with an emphasis on reusable components, scalability, and performance. Cloud integration:
Deploy AI applications on AWS, Azure, or GCP with optimized resource usage and robust CI/CD. Partner with product owners, data scientists, and business SMEs to define requirements and deliver impactful AI products. Set engineering standards and share knowledge across the team, raising the bar on AI and software engineering practice.
Engineering foundation ~8–14 years of software engineering experience, with strong hands-on large-scale Python ~ Working depth in at least one systems or backend language — Go, Rust, Java, or C/C++ — and the judgment to know when to reach for it ~ Strong data structures and algorithms. ~ Hands-on experience building and operating data pipelines and production-grade distributed systems.
Agentic AI and LLMs ~2+ years of hands-on LLM engineering, with at least couple agentic system you designed and took to production ~ Production experience with agent frameworks — LangGraph, Google ADK, CrewAI, Claude Agent SDK, or equivalent — and the fluency to move between them as the ecosystem evolves ~ Experience building MCP (Model Context Protocol) servers and tool-calling interfaces ~ Strong experience with vector databases (Milvus, Pinecone, Weaviate, FAISS, etc. or cloud equivalents) ~ Design of guardrails and reliability patterns — validators, policy checks, self-correction loops, deterministic fallbacks, circuit breakers, and rollback paths
Performance testing and tuning systems against defined SLOs Experience building evaluation frameworks for LLM systems — offline eval sets, continuous online evaluation, and regression detection Cloud Hands-on AWS: containerized services (ECS/EKS), serverless (Lambda), data services (S3, DynamoDB, Redshift) and orchestration (Step Functions); Familiarity with CI/CD pipelines and DevOps practices Experience with managed AI platforms — Amazon Bedrock, Vertex AI, Azure AI — paired with fluency in the underlying fundamentals
Location:
New York City (Hybrid: 2-3 days on-site) Employment Type:
Full Time Employment Turing is hiring a Staff/Principal AI Engineer to lead enterprise-scale agentic AI implementations for Fortune 500 clients. This is a hands-on engineering role focused on designing and shipping autonomous, tool-calling AI systems — agents that reason over enterprise context, invoke real systems through secure interfaces, and operate reliably at scale under strict latency, cost, and governance constraints. You will own these systems end to end: the data pipelines feeding them, the backend services around them, the agent orchestration layer, the evaluation harness that keeps them honest, and the cloud infrastructure they run on. We are looking for engineers with genuine software engineering and data science depth who have taken agentic systems all the way to production.
Lead the architecture and implementation of tool-calling agents that combine retrieval, structured reasoning, and secure action execution with least-privilege access. Productionize LLM applications:
Build retrieval pipelines, prompt synthesis, response validation, and self-correction loops, backed by rigorous evaluation. Own the full stack:
Deliver the data pipelines, backend services, distributed compute, and orchestration layer that agentic systems depend on — not only the model invocation. Build validator models, adversarial test suites, and policy checks; Drive measurable improvements in token efficiency, response time, and unit economics against defined SLOs. Build, maintain, and review high-quality Python and SQL, with an emphasis on reusable components, scalability, and performance. Cloud integration:
Deploy AI applications on AWS, Azure, or GCP with optimized resource usage and robust CI/CD. Partner with product owners, data scientists, and business SMEs to define requirements and deliver impactful AI products. Set engineering standards and share knowledge across the team, raising the bar on AI and software engineering practice.
Engineering foundation ~8–14 years of software engineering experience, with strong hands-on large-scale Python ~ Working depth in at least one systems or backend language — Go, Rust, Java, or C/C++ — and the judgment to know when to reach for it ~ Strong data structures and algorithms. ~ Hands-on experience building and operating data pipelines and production-grade distributed systems.
Agentic AI and LLMs ~2+ years of hands-on LLM engineering, with at least couple agentic system you designed and took to production ~ Production experience with agent frameworks — LangGraph, Google ADK, CrewAI, Claude Agent SDK, or equivalent — and the fluency to move between them as the ecosystem evolves ~ Experience building MCP (Model Context Protocol) servers and tool-calling interfaces ~ Strong experience with vector databases (Milvus, Pinecone, Weaviate, FAISS, etc. or cloud equivalents) ~ Design of guardrails and reliability patterns — validators, policy checks, self-correction loops, deterministic fallbacks, circuit breakers, and rollback paths
Performance testing and tuning systems against defined SLOs Experience building evaluation frameworks for LLM systems — offline eval sets, continuous online evaluation, and regression detection Cloud Hands-on AWS: containerized services (ECS/EKS), serverless (Lambda), data services (S3, DynamoDB, Redshift) and orchestration (Step Functions); Familiarity with CI/CD pipelines and DevOps practices Experience with managed AI platforms — Amazon Bedrock, Vertex AI, Azure AI — paired with fluency in the underlying fundamentals