About the position
At GEICO, we offer a rewarding career where your ambitions are met with endless possibilities. Every day we honor our iconic brand by offering quality coverage to millions of customers and being there when they need us most. We thrive through relentless innovation to exceed our customers’ expectations while making a real impact for our company through our shared purpose. When you join our company, we want you to feel valued, supported and proud to work here. That’s why we offer The GEICO Pledge: Great Company, Great Culture, Great Rewards and Great Careers. The GEICO AI platform and Infrastructure team is looking for a Software Engineer responsible for designing, building, and maintaining Machine Learning platform to support data science modelling initiatives. This responsibility is exciting and opens the opportunity to support Machine Learning Development Lifecycle (MDLC) at GEICO. We are looking for a highly motivated individual with the ability to collaborate with cross-functional teams to ensure seamless integration of various inter-related systems and hybrid (on-prem and/or cloud) technologies. The candidate must have excellent verbal and written communication skills with a proven ability to work independently and in a team environment.
Responsibilities
- Scope, design, and build systems with high scalability, reliability, and resilience
- Support platform initiatives geared toward model deployment, serving, inferencing, and/or monitoring solutions
- Research and implement variety of cloud and/or open-source tools and services across the Model development life cycle ranging from IaC (Infrastructure as code) to self-hosted infrastructure implementation
- Engage with partner teams to debug production issues, pipeline failures, and system latencies
- Engage in cross-functional collaboration with teams of developers, data scientists, product managers, network and security, and other areas throughout the entire MDLC lifecycle
- Lead in design sessions and code reviews with peers
- Collaborate with Engineering teammates to deploy models into production
- Collaborate with regulatory team to develop intended use and regulatory strategy
- Participate in product discussions and roadmap exercises to understand business use cases
- Author technical documentation and reports to communicate process and results
Requirements
- Proficiency in Python for data processing, automation, and ML workflows
- Strong SQL skills for data querying, manipulation, and pipeline development
- Hands-on experience with MLOps platforms such as Jupyter notebooks, MLFlow, Azure Machine Learning, Large Language Model serving and optimizations
- Experience with MLOps practices such as model versioning, model monitoring, and model governance
- Proficiency with Git for version control
- Experience with GitHub Actions or similar CI/CD tools
- Understanding of automated testing and deployment processes
- Understanding of machine learning model lifecycle and deployment challenges
- Experience with cloud computing platforms (AWS, Azure, or GCP)
- Knowledge of software engineering best practices and code quality standards
- Strong problem-solving skills and attention to detail
- Excellent communication skills for cross-functional collaboration
Nice-to-haves
- Experience with Kubernetes for container orchestration
- Familiarity with infrastructure as code tools (Terraform, CloudFormation)
- Knowledge of data workflow orchestration tools (Airflow, Prefect, Argo)
- Experience with monitoring and observability tools (Prometheus, Grafana)
- Understanding of machine learning frameworks (PyTorch, TensorFlow, scikit-learn)
- Experience with stream processing technologies (Kafka, Kinesis)
- Knowledge of data security and compliance requirements
- Working knowledge of networking concepts (DNS/DHCP/Firewalls/Sub-netting, etc.)
- Knowledge of Big Data platforms such as Snowflake, ADLS, Databricks, Cosmos DB
- Knowledge of Big Data processing frameworks and languages such as Spark, Scala
- Experience with at least one IaC (Infrastructure as code) provider, preferably Terraform
- Experience with implementing monitoring and alerting systems to ensure performance and reliability of deployed models
- Experience with infrastructure optimization for cost efficiency, scalability, and reliability
- Knowledge of microservice architecture and distributed systems
- Experience performing Root Cause Analysis (RCA) for application and infrastructure related issues
Benefits
- Opportunity to work on cutting-edge ML infrastructure and tools
- Mentorship from senior engineers and data scientists
- Professional development budget for conferences, courses, and certifications
- Collaborative environment with cross-functional ML teams
- Competitive salary and benefits package
