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Principal AI Engineer (3 days onsite) in New York
Energy Jobline ZR
2 hours ago
Full-time
On-site
Job DescriptionJob Description
What We're Looking For
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 — Go, Rust, Java, or C/C++ — and the judgment to know when to reach for it
Strong 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.
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
RAG from first principles: chunking strategy, embeddings, vector and hybrid retrieval, reranking, and response validation
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
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 full stack:
Deliver the data pipelines, backend services, distributed compute, and orchestration layer 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, 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.
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 on AI and software engineering practice.
What We're Looking For
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 — Go, Rust, Java, or C/C++ — and the judgment to know when to reach for it
Strong 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.
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
RAG from first principles: chunking strategy, embeddings, vector and hybrid retrieval, reranking, and response validation
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
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 full stack:
Deliver the data pipelines, backend services, distributed compute, and orchestration layer 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, 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.
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 on AI and software engineering practice.