Principal AI Engineer

Namely; Mountain View (CA); On-site; USD 130,000 - 170,000; Full time

Job summary

Namely is looking for a Principal AI Engineer to lead the design and scaling of AI-driven systems across various platforms. The ideal candidate should have extensive experience with backend systems, particularly using Java and AWS, while integrating AI functionalities into real-world applications.

You will be tasked with creating scalable, AI-enabled solutions while collaborating with product, architecture, and engineering teams to achieve significant business results. Strong problem-solving capabilities and a technical leadership background are essential.

Qualifications

• 8–10 years of hands-on software engineering experience in production systems.
• Strong backend experience with Java.
• Mandatory AWS experience for cloud-native workloads.
• Expertise in SQL and NoSQL databases.

Responsibilities

• Design and operate AI-powered applications and services.
• Build and maintain cloud-native systems on AWS.
• Create scalable API-first services integrating AI capabilities.
• Lead and mentor engineering teams on AI and cloud best practices.

Skills

Java

AWS

AI Systems

SQL

NoSQL

Docker

Kubernetes

API Development

Tools

Terraform

AWS CDK

Job description

We are seeking a Principal AI Engineer to design, build, and scale AI‑driven systems across our product platforms. This role is ideal for a deeply technical engineer who leads through hands‑on contribution, solves complex problems, and drives the adoption of AI into real‑world products. The ideal candidate brings strong backend and cloud fundamentals, mandatory experience with Java and AWS, and proven experience integrating AI and Generative AI capabilities into production systems. You will work closely with product, architecture, and engineering teams to deliver reliable, scalable, AI‑enabled solutions that create meaningful business impact.

Responsibilities

• AI Engineering System Design: Design, build, and operate AI‑powered applications, including LLM‑based services, ML‑driven workflows, and intelligent automation; integrate AI capabilities into Java‑based backend systems and cloud‑native platforms; design scalable API‑first services (REST, GraphQL) exposing AI capabilities across products; implement data pipelines supporting feature engineering, retrieval‑augmented generation (RAG), vector search, and inference; apply best practices for prompt design, model orchestration, evaluation, and observability.
• Cloud Platform Engineering (AWS – Must Have): Build and operate cloud‑native AI systems on AWS, including EC2, S3, RDS, Lambda, and EKS; design containerized workloads using Docker and Kubernetes (EKS); optimize systems for scalability, latency, reliability, and cost efficiency; implement Infrastructure as Code using Terraform or AWS CDK.
• Data Distributed Systems Design: Design and operate relational (SQL) and NoSQL data stores for high‑scale, distributed systems; make informed trade‑offs around data consistency, availability, and performance; work with event‑driven and asynchronous architectures where appropriate.
• Technical Leadership Impact: Lead complex technical initiatives end‑to‑end, from design through production deployment; influence system design and engineering standards across teams through design reviews and technical guidance; mentor senior engineers and raise the overall bar for AI and cloud engineering excellence; collaborate with Product, Data, UX, Security, and DevOps teams to deliver AI‑driven product capabilities.

Qualifications

• 8–10 years of hands‑on software engineering experience building large‑scale, production systems.
• Strong, hands‑on experience with Java in backend and distributed systems.
• Mandatory, hands‑on experience with AWS, including designing and operating cloud‑native workloads.
• Strong experience with SQL and NoSQL databases in production environments.
• 3+ years of experience building AI‑powered applications, including ML or LLM‑based systems.
• Experience with API‑first architectures (REST, GraphQL).
• Hands‑on experience with Docker and Kubernetes (EKS).
• Strong problem‑solving skills and ability to own systems end‑to‑end.

Additional Experience

• Experience with Generative AI platforms and frameworks (AWS Bedrock, OpenAI APIs, LangChain, LlamaIndex).
• Experience with vector databases and semantic search.
• Familiarity with MLOps practices, model monitoring, and evaluation frameworks.
• Experience working in product‑driven, agile environments.