Sr Lead Software Engineer
On-siteJersey City, New Jersey, United States
Job Summary
Develop secure, high-quality production code for data-intensive applications and lead end-to-end design and implementation of complex software features from requirements through deployment. Drive technical decisions influencing application design, functionality, and reliability while building and maintaining agentic AI systems with multi-agent workflows and human-in-the-loop controls. Own observability, evaluation, and safety of production AI systems including prompt monitoring, output validation, and latency optimization. Mentor junior and mid-level engineers, conduct code reviews, and establish governance for AI-assisted engineering practices across teams to improve delivery speed and operational outcomes.
Required Qualifications
- Formal training or certification on software engineering concepts
- 5+ years applied experience
- Hands-on experience building and shipping LLM-based applications and agentic systems with tool use, memory, and multi-step reasoning in production environments
- Advanced proficiency in one or more programming languages, particularly Python and/or Java
- Deep experience with large-scale data processing, microservices, API design, and event streaming (Kafka)
- Working knowledge of relational and NoSQL databases, vector stores, and data lake architectures
- Experience with caching technologies (Redis, MemCached), observability tools (Dynatrace, Splunk, Grafana), and orchestration frameworks (Airflow, Temporal)
- Proficiency in CI/CD, test-driven development, automation, and all aspects of the Software Development Lifecycle
- Strong understanding of agile methodologies, application resiliency, and security best practices
- Practical cloud-native engineering experience (AWS, Azure, or GCP)
- Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls
Desired Qualifications
- Experience with LLM orchestration frameworks
- Hands-on experience with model serving infrastructure or managed endpoints (AWS Bedrock, Azure OpenAI)
- Familiarity with AI evaluation and observability practices: evals frameworks, red-teaming, prompt drift detection, and cost/latency monitoring
- Understanding of agentic design patterns and how to constrain agent autonomy in high-stakes financial workflows
- Experience with modern data platforms such as Databricks or Snowflake
- Hands-on experience with Spark/PySpark and big data processing at scale
- Knowledge of the financial services industry and its technology systems
- Awareness of AI risk and regulatory considerations relevant to AI use in financial decision-making
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