Praxent is a company focused on transforming the financial services sector by modernizing outdated software applications. As a Principal Software Engineer, you will lead development teams to establish technical standards and architecture for AI solutions, ensuring they align with business goals and deliver high-impact results.
Responsibilities:
- Lead development teams to ensure sprint commitments are met
- Establish Technical Standards & Architecture
- Define and maintain AI technical standards, reference architectures, prompt libraries, templates, and playbooks
- Support the creation and evolution of AI enabled SDLC frameworks with developers that teams can reliably apply across projects
- Lead architecture reviews and act as a quality gate for AI design decisions
- Support Presales AI Solutioning
- Partner with sales and delivery to design AI strategies, architectures, and proof of concepts
- Translate client needs into compelling technical narratives and implementation plans
- Serve as a trusted technical voice during early stage client engagements
- Design & Implement MLOps Platforms
- Architect and deploy MLOps/AIOps platforms that support model lifecycle management, evaluation, and monitoring
- Optimize performance, cost, and reliability across AI deployments
- Ensure AI solutions are secure, observable, and production ready
- Elevate Training & Delivery Quality
- Develop and deliver AI training programs, workshops, and internal documentation
- Maintain AI standards and learning content within internal platforms
- Mentor engineers and delivery leads on AI trade-offs, risks, and best practices
- Define AI architecture patterns and review project implementations
- Create reusable AI frameworks, prompts, and playbooks
- Support presales efforts with technical designs and client-facing explanations
- Coach teams through architectural decisions and delivery challenges
- Collaborate cross-functionally to align AI initiatives with business outcomes
Requirements:
- Deep experience designing and delivering AI enabled systems in production
- Strong background in MLOps, model lifecycle management, and evaluation frameworks
- Proficiency with modern AI/ML frameworks and cloud based AI platforms
- 8–12+ years of professional experience in software engineering and system architecture
- Proven ability to design scalable, secure, and maintainable systems
- In depth experience with end-to-end full stack architectures, modernizing legacy systems into scalable, cloud-ready solutions while leveraging AI/ML capabilities (e.g., LLMs, automation, intelligent workflows) to improve performance, maintainability, and business outcomes
- Experience improving delivery efficiency through standards and tooling
- Comfortable presenting complex AI concepts to technical and non-technical stakeholders
- Ability to connect AI capabilities to measurable business value
- Demonstrated experience mentoring engineers and elevating team capabilities
- Passion for building shared understanding and continuous improvement
- Relevant Education May include intensive programs, Bachelor's degree in Computer Science, Engineering, or equivalent experience
- Values Alignment: We Care Deeply, Always Deliver, Never Settle, Do It Together, Own the Outcome, and approach every situation with a CAN DO mentality