Senior AI/ML Engineer

CloudGeometry; Palo Alto (CA); On-site; CAD 120,000 - 180,000; Full time

Benefits offered by this job

Competitive compensation

Zero legacy infra

Training budget

Access to frontier model APIs

Collaborative team

Job summary

CloudGeometry is seeking a Senior AI/ML Engineer to join our flagship AI Platform for life sciences, accelerating drug discovery through cloud and AI. You will design and deploy production AI systems—from autonomous agents to classical ML pipelines and enterprise AI gateways—across a distributed, remote team.

You will build multi-agent systems, deploy AI gateways, and craft end-to-end deployment pipelines with drift monitoring.

Qualifications

• 5+ years in software/AI engineering, including 3+ years focused on AI/ML in production.
• 2+ years deploying classical ML models and/or LLM-based systems at scale.
• Hands-on experience with AI agent frameworks and/or AI Gateway infrastructure.
• Strong Python skills; working knowledge of TypeScript/Node.js for backend APIs.
• Experience with Databricks (Spark, Delta Lake, MLflow, Unity Catalog) and AWS (ECS, Lambda, SageMaker, S3).
• Excellent English communication — able to explain ML systems to both technical and non-technical stakeholders.
• Comfortable working autonomously in a remote team (9 AM–5 PM EST overlap required).

Responsibilities

• Build and deploy multi-agent AI systems (LangGraph, AutoGen, or similar), including tool-calling and MCP servers/clients.
• Deploy AI Gateway infrastructure to manage LLM routing, cost, and security across providers (OpenAI, Anthropic, Bedrock, Vertex).
• Build end-to-end deployment pipelines (Databricks/SageMaker) with monitoring for drift and performance.
• Design RAG pipelines and fine-tune open-source LLMs (LoRA/QLoRA) for domain-specific use cases.
• Lead architecture reviews, mentor on MLOps best practices, and collaborate with data engineers on lakehouse pipelines.
• Participate in daily Scrum with a globally distributed team.

Skills

AI/ML production

Python

TypeScript/Node.js

Databricks

AWS

English communication

Remote collaboration

Scrum/daily standups

Tools

Databricks

AWS

SageMaker

S3

ECS

LangGraph

AutoGen

Job description

A Silicon Valley-based cloud system integrator working with AWS, Google, and Databricks. We're looking for a Senior AI/ML Engineer to join our flagship AI Platform for life sciences — supporting companies like Pfizer, Moderna, and Novartis in accelerating drug discovery through cloud and AI. You'll help design and deploy production AI systems, from autonomous agents to classical ML pipelines and enterprise AI gateways.

What You'll Do

• Build and deploy multi-agent AI systems (LangGraph, AutoGen, or similar), including tool-calling and MCP (Model Context Protocol) servers/clients.
• Deploy AI Gateway infrastructure to manage LLM routing, cost, and security across providers (OpenAI, Anthropic, Bedrock, Vertex).
• Build end-to-end deployment pipelines (Databricks/SageMaker) with monitoring for drift and performance.
• Design RAG pipelines and fine-tune open-source LLMs (LoRA/QLoRA) for domain-specific use cases.
• Lead architecture reviews, mentor on MLOps best practices, and collaborate with data engineers on lakehouse pipelines.
• Participate in daily Scrum with a globally distributed team.

What You Bring

• 5+ years in software/AI engineering, including 3+ years focused on AI/ML in production.
• 2+ years deploying classical ML models and/or LLM-based systems at scale.
• Hands-on experience with AI agent frameworks and/or AI Gateway infrastructure.
• Strong Python skills; working knowledge of TypeScript/Node.js for backend APIs.
• Experience with Databricks (Spark, Delta Lake, MLflow, Unity Catalog) and AWS (ECS, Lambda, SageMaker, S3).
• Excellent English communication — able to explain ML systems to both technical and non-technical stakeholders.
• Comfortable working autonomously in a remote team (9 AM–5 PM EST overlap required).

Nice to Have

• AWS ML Specialty, Databricks ML Professional, or Google ML Engineer certification.
• Experience fine-tuning open-source LLMs (Llama, Mistral, Falcon).
• Familiarity with GxP/21 CFR Part 11 compliance in life sciences.
• Contributions to open-source AI/ML or MCP tooling.

What We Offer

• Competitive compensation and benefits.
• Zero legacy infrastructure — work on cutting-edge AI systems.
• Training budget (certifications, hackathons, Udemy/Coursera).
• Access to Claude Code, Codex, Cursor, and frontier model APIs.
• A collaborative team at the intersection of life sciences and applied AI.