Principal Software Engineer

Salary: $61,000 - 101,000 per year Requirements:

• We require formal training or certification in software engineering concepts, along with 7+ years of applied experience.
• We need hands-on experience delivering system design, application development, testing, and operational stability at enterprise scale.
• We require practical experience building and deploying production AI/ML systems, including LLM-based applications and agentic architectures with tool use, memory, and multi-step reasoning in regulated environments.
• We expect expert-level proficiency in one or more programming languages, especially Python and/or Java.
• We need advanced knowledge of software development and technical processes, with strong depth in one or more disciplines such as cloud, AI/ML, or data engineering.
• We require experience with large-scale data processing, microservices, API design, Kafka, Redis, MemCached, observability tools such as Dynatrace, Splunk, and Grafana, and orchestration frameworks such as Airflow and Temporal.
• We need advanced working knowledge of relational and NoSQL databases, vector stores, data lake architectures, and data governance.
• We require practical cloud-native experience with AWS, Azure, or GCP.
• We need the ability to present clearly and communicate effectively with senior leaders and executives.
• We require a background in Computer Science, Computer Engineering, Mathematics, or a related technical field.
• We value demonstrated experience leading adoption of agentic AI-enabled development practices across teams, including standards for human-in-the-loop validation, traceability, auditability, and secure handling of sensitive data.
• We expect a strong understanding of responsible AI use and control expectations in engineering workflows, including security, resiliency, data sensitivity, and risk-based governance.

• We architect and implement complex, scalable engineering frameworks and solutions using modern software design principles.
• We develop secure, high-quality production code for data-intensive applications and platforms, while reviewing and mentoring other engineers.
• We create durable, reusable software frameworks and patterns that can be used across teams and functions.
• We design and govern agentic AI systems, including multi-agent workflows, tool integrations, and human-in-the-loop controls suited to regulated financial services environments.
• We establish engineering standards for LLM-based applications, including RAG pipelines, embedding workflows, vector store integrations, and model serving, with a focus on safety, observability, and reproducibility at scale.
• We drive adoption of advanced technical methods and practices aligned with current industry standards and product development approaches.
• We serve as the go-to subject matter expert in one or more areas within data engineering, platform architecture, or AI systems.
• We advise cross-functional teams on technology decisions within our area of expertise.
• We influence leaders and senior stakeholders across business, product, and technology teams on technical strategy and direction.
• We architect and govern agentic AI-enabled engineering workflows using enterprise-authorized tools to improve delivery speed, code quality, and operational outcomes at scale, while defining guardrails for validation, security, resiliency, and reuse.
• We apply knowledge of tools within the software development lifecycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to increase the value delivered through automation at scale.

• Agentic AI
• AI
• Airflow
• API
• AWS
• Architect
• Azure
• Cloud
• Dynatrace
• GCP
• Grafana
• Support
• Java
• Kafka
• LLM
• Marketing
• Model Serving
• NoSQL
• Python
• RAG
• Redis
• Security
• Splunk
• microservices
• Big Data
• Datab