Principal AI Engineer

NAM Info Inc; New York (NY); Hybrid; USD 210,000 - 320,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

NAM Info Inc. in NYC is hiring a Staff/Principal AI Engineer to lead enterprise-scale agentic AI implementations for Fortune 500 clients. This hands-on role designs autonomous tool‑calling AI systems that reason over enterprise data and operate at scale with strict latency, cost, and governance needs.

You will own data pipelines, backend services, orchestration, evaluation harness, and cloud infra end‑to‑end, guiding production deployments and mentoring other engineers.

Qualifications

• 15+ years of software engineering experience with hands-on large-scale Python.
• Strong systems language experience (Go, Rust, Java, or C/C++).
• Solid data structures and algorithms; APIs and microservices expertise.
• Hands-on data pipelines and production‑grade distributed systems.
• 2+ years of hands‑on LLM engineering and agentic systems production.

Responsibilities

• Lead end‑to‑end agentic AI systems from data pipelines to cloud infrastructure.
• Design and ship tool‑calling agents with secure, scalable interfaces.
• Own evaluation harness, governance, and reliability patterns.
• Collaborate with Fortune 500 clients and cross‑functional teams.
• Mentor engineers and raise engineering standards across the team.

Skills

Python

Go

Rust

Java

C/C++

APIs & microservices

Distributed systems

Data pipelines

LLM engineering

Tool calling

Tools

LangGraph

Google ADK

CrewAI

Claude Agent SDK

Milvus

Pinecone

Weaviate

FAISS

LangSmith

Langfuse

Terraform

ECS/EKS

S3

DynamoDB

Redshift

Job description

Location: US(NYC)- NYC – Hybrid, 3 Days WFO

Experience Level: Staff/Principal (15+ Years)

About the Role

Client 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.

What We're Looking For

• 15+ 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.
• 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

Optimization

• Deep familiarity with token optimization and context‑window management — context shaping, pruning, and compaction
• Latency and cost optimization through caching, model routing, batching, streaming, and parallel tool calls
• Performance testing and tuning systems against defined SLOs

Evaluation

• Experience building evaluation frameworks for LLM systems — offline eval sets, continuous online evaluation, and regression detection
• Instrumentation and traceability suitable for regulated enterprise environments using tools like LangSmith, Langfuse, etc.

Cloud

• Hands‑on AWS: containerized services (ECS/EKS), serverless (Lambda), data services (S3, DynamoDB, Redshift) and orchestration (Step Functions); Azure or GCP equivalents also valued
• Familiarity with CI/CD pipelines and DevOps practices
• Infrastructure as code with Terraform or CloudFormation, and mature CI/CD practice

Working traits

• Strong analytical problem‑solving with a bias to ownership and urgency
• Clear cross‑team communication, working directly with client stakeholders to translate business problems into technical roadmaps
• Able to work productively in ambiguity from system‑level documentation and ramp quickly in unfamiliar codebases

Good to Have

• Experience with managed AI platforms — Amazon Bedrock, Vertex AI, Azure AI — paired with fluency in the underlying fundamentals
• 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.