Key skills
TypeScriptPythonAILLMOpenAIAnthropicRAGLangChainLlamaIndexAgenticPyTorchMLOpsLangGraphVector DatabasePineconeWeaviateAWSAzureGCPTerraformKubernetesDockerGitHubCI/CD
About this role
Turing is the world's leading research accelerator for frontier AI labs and a trusted partner for global enterprises. They are seeking a Principal AI Engineer to design and ship full-stack AI applications, architect autonomous workflows, and serve as the technical face for demanding accounts.
Responsibilities:
- Design and ship full-stack AI applications. You own the pipeline from data ingestion to the LLM inference layer to the frontend interface
- Architect autonomous agentic workflows (using frameworks like LangChain, LangGraph, or custom implementations) that can reason, plan, and execute complex tasks
- Write robust, clean, and highly testable code (Python/Typescript). "It works on my machine" is not acceptable; it must work in the customer's restricted cloud environment
- Handle DevOps and MLOps constraints: Dockerizing agents, managing Kubernetes clusters, and optimizing inference latency
- Serve as the technical face of the company for our most demanding accounts
- Navigate complex stakeholder environments. You will push back on unrealistic demands, clarify ambiguous requirements, and steer "tricky" customers toward technically feasible solutions without breaking rapport
- Translate technical constraints into business value for non-technical executives
- Generate high-quality data for our customers and ability to review the data generated by other experts. This is one of the most critical aspects of the role
Requirements:
- 8–12 years of total engineering experience
- B.S./M.S. in Computer Science, Math, or Physics from a top-tier institution (IIT, MIT, Stanford, Harvard, Berkeley, CMU, or similar global top-ranking universities)
- Deep AI Fluency: understanding of RAG implementation nuances, vector database optimization, fine-tuning (LoRA/PEFT), and the architecture of agentic loops
- Experience with end-to-end deployment (AWS/GCP/Azure, CI/CD pipelines, Terraform, Docker)
- Ability to design and ship full-stack AI applications
- Ability to architect autonomous agentic workflows (using frameworks like LangChain, LangGraph, or custom implementations)
- Ability to write robust, clean, and highly testable code (Python/Typescript)
- Ability to handle DevOps and MLOps constraints: Dockerizing agents, managing Kubernetes clusters, and optimizing inference latency
- Ability to serve as the technical face of the company for demanding accounts
- Ability to navigate complex stakeholder environments
- Ability to translate technical constraints into business value for non-technical executives
- Ability to generate high-quality data for customers and review data generated by other experts
- Public Code: a visible GitHub footprint is very helpful
- Experience with AI/Data technologies: PyTorch, LangChain, LlamaIndex, Pinecone/Weaviate, OpenAI/Anthropic APIs, HuggingFace
- Experience with Infra technologies: Kubernetes, Terraform, AWS/GCP