Senior AI Engineer

Senior AI Engineer

We are hiring a Senior AI Engineer to lead the development of our cloud-based (Google Cloud) commercial AI products. You will bridge the gap between experimental data science prototypes and production-grade software. You will architect the robust systems and LLMOps workflows necessary to transform AI models into reliable, enterprise-ready applications. By designing stable backend architectures and seamless integration layers, you will ensure our AI solutions are not just functional, but ethical, efficient, and high-value products that meet the rigorous demands of our commercial clients.

About the role

The Senior AI Engineer will lead the implementation of rigorous evaluation frameworks to monitor model performance, drift, and cost in real-time. They will architect and develop high-performance backend services and APIs using Python (FastAPI) to serve large language models at scale. They will design advanced Retrieval-Augmented Generation (RAG) systems, selecting and managing vector databases and optimizing embedding strategies for accuracy and speed. They will establish comprehensive model observability and guardrail systems to monitor real-time performance, detect distribution drift, and implement automated safety filters that mitigate hallucinations, bias, and toxic outputs in production environments. They will build robust integration layers that connect AI agents securely to external enterprise systems, CRMs, and legacy databases. They will conduct code reviews, provide technical guidance, and foster a culture of continuous learning and innovation within the engineering team. They will collaborate with infrastructure teams to define deployment strategies, ensuring solutions scale dynamically under load. They will define the end-to-end architecture for AI products on cloud platforms (preferably Google Cloud Platform), ensuring high availability, security, and cost-effectiveness.

Responsibilities

• Lead the implementation of rigorous evaluation frameworks to monitor model performance, drift, and cost in real-time.
• Architect and develop high-performance backend services and APIs using Python (FastAPI) to serve large language models at scale.
• Design advanced Retrieval-Augmented Generation (RAG) systems, selecting and managing vector databases and optimizing embedding strategies for accuracy and speed.
• Establish comprehensive model observability and guardrail systems to monitor real-time performance, detect distribution drift, and implement automated safety filters that mitigate hallucinations, bias, and toxic outputs in production environments.
• Build robust integration layers that connect AI agents securely to external enterprise systems, CRMs, and legacy databases.
• Conduct code reviews, provide technical guidance, and foster a culture of continuous learning and innovation within the engineering team.
• Collaborate with infrastructure teams to define deployment strategies, ensuring solutions scale dynamically under load.
• Define the end-to-end architecture for AI products on cloud platforms (preferably Google Cloud Platform), ensuring high availability, security, and cost-effectiveness.

Requirements

• 10+ years of software engineering experience with at least 3 years dedicated to AI/ML application development.
• Expert proficiency in Python AI application development and modern API architecture (REST, GraphQL, gRPC) using enterprise standards like static type checking and data validation.
• Deep experience building production applications with LLM frameworks such as LangChain, LangGraph or LlamaIndex.
• Hands-on expertise with vector databases (Pinecone, Weaviate, PostgreSQL) and search algorithms.
• Strong understanding of LLMOps principles, including model registry, versioning, and serving infrastructure specifically in Google Cloud.
• Optional: Experience in Typescript development for prototyping and integrations
• Optional: Proficiency with git workflows and understanding of standard application development processes

Qualifications

• Knowledge of advanced prompt engineering and fine-tuning techniques (LoRA, PEFT).
• Experience optimizing inference costs and latency for large-scale deployments.
• Previous experience in a client-facing consulting role, managing diverse stakeholders and navigating complex organizational structures.
• Any Google Cloud Professional Certification

Preferred Qualifications

• Knowledge of advanced prompt engineering and fine-tuning techniques (LoRA, PEFT).
• Experience optimizing inference costs and latency for large-scale deployments.
• Previous experience in a client-facing consulting role, managing diverse stakeholders and navigating complex organizational structures.
• Any Google Cloud Professional Certification

Pay

Our total rewards program is designed for your protection, peace of mind, and overall well-being. In addition to our outstanding basics, we offer a net-zero cost medical option, company contributions to your HSA, fertility support, fully-paid parental leave, a monthly stipend for your lifestyle spending account, and much more.

Schedule

Our ideal candidate is comfortable working remotely, hybrid, or on-site.