Artificial Intelligence Engineer

Principal AI Engineer Location: New York City (Hybrid: 2–3 days on-site) Employment Type: Full-Time Employment Experience Level: Staff/Principal (8–14 years) Salary Range: $200K–$300K base About the Role 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. Roles & Responsibilities Design and build agentic systems: 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 AI application stack: Deliver the data pipelines, backend services, distributed compute, and orchestration layers that agentic systems depend on—not only the model invocation. Engineer for reliability and governance: Build validator models, adversarial test suites, and policy checks; enforce deterministic fallbacks and rollback strategies; instrument continuous evaluation. Optimize for cost and latency: Drive measurable improvements in token efficiency, response time, and unit economics against defined SLOs. Codebase ownership: Build, maintain, test, and review high-quality Python and SQL, emphasizing reusable components, scalability, reliability, and performance. Cloud integration: Deploy AI applications on AWS, Azure, or GCP with optimized resource usage and robust CI/CD. Cross-functional collaboration: Partner with product owners, data scientists, and business SMEs to define requirements and deliver impactful AI products. Mentoring and technical leadership: Set engineering standards and share knowledge across the team, raising the bar for AI and software engineering practices. Hands-On Coding Expectations This role has a high hands-on engineering bar. Candidates should be prepared to demonstrate their ability to independently build a functioning Python solution during a live, practical coding exercise with minimal interviewer guidance. The assessment will focus on actual implementation—not solely on architecture discussions or explanations of previous AI projects. While the specific exercise may vary, candidates should be comfortable working with core Python concepts such as object-oriented programming, classes and constructors, type hints, HTTP/API calls, JSON parsing, loops, exception handling, debugging, and organizing or analyzing results with tools such as pandas. Evaluation will consider whether the solution runs successfully, how errors and edge cases are handled, the structure and quality of the code, and the candidate’s ability to clearly explain technical decisions. Candidates should be comfortable coding independently without relying on AI assistants such as ChatGPT or GitHub Copilot. Additional technical discussions may cover Pydantic, LangGraph and agent-state management, RAG architecture, vector-retrieval fundamentals, tracing and evaluation, and production reliability patterns. What We’re Looking For Engineering Foundation 8–14 years of software engineering experience, with strong hands-on experience building large-scale applications in Python. Working depth in at least one systems or backend language—Go, Rust, Java, or C/C++—and the judgment to know when to use it. Strong knowledge of data structures and algorithms. Strong understanding of APIs, microservices, and system design. Hands-on experience building and operating data pipelines and production-grade distributed systems. Ability to independently write, debug, test, and explain clean, functioning production-quality code. Agentic AI and LLMs 2+ years of hands-on LLM engineering experience, including at least two agentic systems that you designed and took to production. Production experience with agent frameworks such as LangGraph, Google ADK, CrewAI, Claude Agent SDK, or equivalent, with the fluency to move between frameworks as the ecosystem evolves. Experience building MCP (Model Context Protocol) servers and tool-calling interfaces. RAG experience from first principles, including chunking strategy, embeddings, vector and hybrid retrieval, reranking, and response validation. Strong experience with vector databases such as Milvus, Pinecone, Weaviate, FAISS, or cloud equivalents. Experience designing guardrails and reliability patterns, including validators, policy checks, self-correction loops, deterministic fallbacks, circuit breakers, and rollback paths. Optimization Deep familiarity with token optimization and context-window management, including context shaping, pruning, and compaction. Latency and cost optimization through caching, model routing, batching, streaming, and parallel tool calls. Experience testing and tuning system performance against defined SLOs. Evaluation Experience building evaluation frameworks for LLM systems, including offline evaluation sets, continuous online evaluation, and regression detection. Instrumentation and traceability suitable for regulated enterprise environments using tools such as LangSmith, Langfuse, or equivalent. Cloud Hands-on AWS experience, including containerized services such as ECS/EKS, serverless services such as Lambda, data services such as S3, DynamoDB, and Redshift, and orchestration through Step Functions. Azure or GCP equivalents are also valued. Familiarity with CI/CD pipelines and mature DevOps practices. Infrastructure as code using Terraform or CloudFormation. Working Traits Strong analytical problem-solving skills with a bias toward ownership and urgency. Clear cross-team communication and the ability to work directly with client stakeholders to translate business problems into technical roadmaps. Ability to work productively through ambiguity, understand system-level documentation, and ramp quickly in unfamiliar codebases. Good to Have Experience with managed AI platforms such as Amazon Bedrock, Vertex AI, or Azure AI, paired with fluency in the underlying fundamentals. Full-stack application development experience, including modern frontend frameworks such as React, Angular, or Vue, backend APIs and services, databases, and cloud deployment. Show more Show less