Overview
Full Time
Contract - W2
Contract - Independent
Contract - of contract
Skills
python
kubernetes
Scaling
Generative AI
2 years of hands-on AI Engineering experiencen
Experience building
managing and monitoring ML pipelines
Solid understanding of machine learning
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.
Job Details
Job Title: AI Scientist
Location: San Francisco, CA or NYC | Hybrid
Duration: Full Time
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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