Lead Software Engineer - ML Engineer for Agent Platform
On-siteJersey City, New Jersey, United States
Job Summary
Build and operate major NEO runtime components including agent execution, sandboxing, memory layers, and retrieval systems while executing creative software solutions and technical troubleshooting. Develop secure, high-quality production code, review peer work, and drive team adoption of enterprise-authorized AI-assisted engineering practices to improve delivery speed and operational outcomes. Lead evaluation sessions with external vendors to probe architectural designs and implement permission-aware, auditable execution for agents. Mentor senior engineers, foster inclusive team culture, and apply advanced agile methodologies to ensure system stability across the financial services domain.
Required Qualifications
- Formal training or certification on software engineering concepts
- 5+ years applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Advanced in one or more programming language(s)
- Strong Python
- Demonstrated experience leading effective use of approved AI-assisted software development tools
- Ability to set team expectations for validating AI outputs for correctness, performance, and security
- Strong understanding of responsible AI use in engineering workflows
- Experience coaching engineers on safe, compliant adoption within delivery practices
- Hands-on experience building LLM-power or agentic systems
- Experience with tracing, evaluations, and guardrails
- Proficient in all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- Demonstrated proficiency in software applications and technical processes within a technical discipline
- In-depth knowledge of the financial services industry and their IT systems
- Practical cloud native experience
- Production Kubernetes
Desired Qualifications
- Exposure to LLMs
- RAG architectures
- vector databases
- embedding-based retrieval systems
- Graph RAG
- Experience with agent protocols (A2A, MCP) or multi-agent orchestration
- Experience with sandboxed/secure code execution
- Experience with agent memory
- graph-backed retrieval
- Familiarity with building or running evals for LLM/agent systems
- Proficiency with Infrastructure as Code (Terraform)
- containerized deployments (Docker, Kubernetes)
- Experience with data observability, quality, and metadata management tools
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