AI Scientist/Engineer - Capital Markets

  • San Francisco, CA
  • Posted 8 hours ago | Updated 8 hours ago

Overview

Hybrid
$140,000 - $250,000
Full Time

Skills

Azure
Artificial Intelligence
Machine Learning

Job Details

AI Engineer/Scientist - Capital Markets

Duration: Full Time

Interview Process: Video

Location: Hybrid San Francisco, CA No Relocation Candidates must be onsite day one and go into the office three times a week.

AI Engineers with extensive experience in advanced AI capabilities to power Research Digitization, Banking and Global Market solutions as well as Evaluation and subsequent implementation of Data/Model Parallelism libraries and techniques, AI Observability & Monitoring solutions, Vector databases and inference engines. Analyze ML and data processing workloads to identity latency contributors, inefficient compute utilization and provide remediation recommendations and Automate AI infrastructure provisioning.

Job Description: Capital Markets Quantitative & Technology Services - Data AI and Research Technology (DART) team is hiring a hands-on AI Engineer to build, manage and deploy cutting edge AI solutions for Investment Research, Banking, Sales and Trading.
What will you do?

  • Engineer advanced AI capabilities to power Research Digitization, Banking and Global Market solutions
  • Evaluation and subsequent implementation of Data/Model Parallelism libraries and techniques, AI Observability & Monitoring solutions, Vector databases and inference engines
  • Deploy, manage, performance tune and scale containerized applications using Kubernetes. Architect clusters leveraging fit for purpose hardware for AI workloads
  • Provide subject matter expertise in distributed and parallel computing. Analyze ML and data processing workloads to identity latency contributors, inefficient compute utilization and provide remediation recommendations
  • Automate AI infrastructure provisioning
  • Create reports for AI infrastructure usage and cost reports
  • Collaborate with multi-functional geographically distributed teams to drive cutting edge AI infrastructure and solutions

What do you need to succeed?

Must Have

  • Minimum of 2 years of hands-on AI Engineering experience
  • Degree in Computer Science or Engineering
  • Experience building, scaling, managing and monitoring ML pipelines
  • Experience with Python, Kubernetes, distributed computing
  • Solid understanding of machine learning, generative AI, agents, multi-agent collaboration, MCP servers, with focus on real-world implementation
  • Proficient in CI/CD principles, version control and best practices for deploying AI workloads to production.

Nice to Have

  • Azure Databricks, Spark, Snowflake, NVIDIA NIM, MLFlow
  • Experience building and deploying MCP Servers, AI Agents
  • Experience with any vector database.
  • Experience with Terraform
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