Senior AI Engineer

What experience should you have

• 10+ years of years experience developing and deploying Data Science and ML solutions
• 3 years of experience working directly with GenAI/RAG/LLM architecture
• 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.
• Nice to have: Experience in Typescript development for prototyping and integrations
• Nice to have: Proficiency with git workflows and understanding of standard application development processes

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

What you’ll be doing in this role

• 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.

What you’ll need to accomplish in your first year

• Develop reusable internal libraries and architectural patterns and standards to accelerate the delivery of AI solutions across multiple client engagements.
• Mentor engineers on best practices for building deterministic software around probabilistic AI models.

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

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