AI Scientist/Capital Markets

  • San Francisco, CA
  • Posted 1 day ago | Updated 1 day ago

Overview

On Site
$200,000 - $220,000
Full Time

Skills

Microsoft Azure
Generative Artificial Intelligence (AI)
Kubernetes
Machine Learning (ML)
Management
Data Modeling
Data Processing
Recruiting
Research
Sales
Servers
Modeling
Database
Databricks
Evaluation
FOCUS
Microsoft Certified Professional
Apache Spark
Artificial Intelligence
Banking
Capital Market
Collaboration
Parallel Computing
Productivity
Provisioning
Python
Snow Flake Schema
Computer Hardware
Computer Science
Continuous Delivery
Continuous Integration
Version Control
NIM
System Integration Testing
Terraform
Trading
Vector Databases

Job Details

Looking for a job? Urgent role awaits!

AI Scientist/Capital Markets

Location: Hybrid San Francisco, CA or NYC

CANDIDATES CAN SIT IN SAN FRANCISCO OR MIDTOWN NYC. IMMEDIATE NEED!

The Candidates We Sent Before Were Too High Level And Not Enough Hands-On. These Positions are very hands-on and require at Least 80% hands on codingThis Position Is Heavily Focused on Model and Modeling Trading Systems and Process to Improve Performance and Productivity.

**We A senior (10+ Years) 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

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

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