Our Service Engineering team at GEICO is revolutionizing customer service using AI and multi-agent systems. If you're passionate about creating large-scale, distributed AI applications with significant business impact, this opportunity is perfect for you. Our focus is on enhancing self-service rates across all communication channels, from voice (IVR) to chat.

Our Senior Engineer is a leading member of the engineering staff working across the organization to provide friction-less experience for our customers and maintain the highest standards of protection and availability. Our team thrives and succeeds in delivering high-quality technology products and services in a hyper-growth environment where priorities shift quickly. The ideal candidate has broad and deep technical knowledge, typically ranging from front-end UIs through back-end systems and all points in between.  

As a Senior Engineer, you will:

• Design, develop, and deploy large-scale distributed AI applications that power customer self-service across multiple communication channels (voice, IVR, chat).
• Build and optimize multi-agent systems that enable intelligent, collaborative decision-making to improve automation and customer experience.
• Collaborate with cross-functional teams (engineering, product, data science) to translate business requirements into scalable AI/ML solutions.
• Ensure system reliability, scalability, and performance through best practices in architecture, testing, and monitoring.
• Stay at the forefront of AI and distribute systems research, bringing innovative approaches and tools into production environment.
• Utilize programming languages like Python, SQL, and NoSQL databases, Container Orchestration services including Docker and Kubernetes, and a variety of Azure tools and services
• Consistently share best practices and improve processes within and across teams

Qualifications

• Hands-on proficiency with modern AI/ML frameworks and tools (e.g., TensorFlow, PyTorch) and programming languages such as Python, Java, or C++.
• Proven experience designing, developing, and deploying AI or machine learning models (LLMs) in production environments, with a focus on scalability and performance.
• Strong software engineering background with expertise in building large-scale distributed systems, preferably in cloud environment (Azure or AWS).
• Demonstrated ability to apply AI/ML solutions to real-world business problems, delivering measurable impact in areas such as natural language processing, speech recognition, recommendation systems, or intelligent automation.
• Experience in building products using micro-service-oriented architecture and extensible REST APIs
• Experience building the architecture and design (architecture, design patterns, reliability, and scaling) of new and current systems
• Experience with continuous delivery and infrastructure as code
• Experience in leveraging PowerShell scripting
• Experience in existing Operational Portals such as Azure Portal
• Experience with application monitoring tools and performance assessments.
• Ability to excel in a fast-paced, startup-like environment
• Knowledge of developer tooling across the software development life cycle (task management, source code, building, deployment, operations, real-time communication)

Experience

• 4+ years of professional software development experience within a Python or Java framework (J2EE, web containers and Java)
• 4+ years of experience in open-source frameworks
• 3+ years of experience with architecture and design
• 3+ years of experience with AWS, GCP, Azure, or another cloud service

Education

• Bachelor’s degree in computer science, Information Systems, or equivalent education or work experience

Annual Salary

The above annual salary range is a general guideline. Multiple factors are taken into consideration to arrive at the final hourly rate/ annual salary to be offered to the selected candidate. Factors include, but are not limited to, the scope and responsibilities of the role, the selected candidate’s work experience, education and training, the work location as well as market and business considerations.