Staff Engineer - AI Platform

Berkley•Indianapolis, IN

About The Position

The AI Platform, Principal Engineer is responsible for designing, building, and maintaining robust AI platforms that support advanced machine learning and artificial intelligence applications. The role works closely with data scientists, software engineers, and product managers to develop scalable solutions that improve the efficiency, reliability, and performance of AI systems. The position develops AI platform capabilities, data pipelines, model deployment strategies, monitoring systems, and backend services. It also supports high availability, security, and reliability; integrates with databases and third-party services; and provides technical guidance to teams using the platform. The role expects engineers to incorporate AI-assisted tools into their daily work as a natural extension of their expertise. The position requires 10%–20% travel. A bachelor’s degree in computer science, information technology, information systems, or a related discipline is requested, with equivalent experience or alternative qualifications considered.

Requirements

10+ years of progressive engineering experience developing or engineering solutions and leading multiple large-scale data-domain or cross-domain engineering initiatives.
Proven experience building and maintaining AI or machine learning platforms.
Strong knowledge of Python, R, and/or Java.
Experience with cloud platforms such as AWS or Azure and containerization technologies such as Docker and Kubernetes.
Experience optimizing code and infrastructure for large dataset manipulation.
Excellent knowledge of MySQL, SQL Server, PostgreSQL, MongoDB, and/or CosmosDB.
Excellent knowledge of modern vector and graph databases.
Excellent knowledge of Redis cache or similar solutions.
Experience with Git and GitLab and Agile development methodologies.
Excellent understanding of NLP and AI/ML design and implementation concepts.
Excellent understanding of REST APIs, API gateways, and SPAs.
Excellent problem-solving, communication, and collaboration skills, including ability to work in a fast-paced environment.
Excellent knowledge of data storage solutions and database management systems.
Proficiency in ORM, advanced algorithms, data structures, object-oriented and functional design principles, and best-practice patterns.
Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, and Scikit-learn.
Multiple years working with structured and unstructured data in a cloud-based data environment, such as Redshift, Databricks, Hadoop, Snowflake, Spark, or unmanaged Kafka.
Experience with Master Data Management (MDM) systems and processes.
Experience with Data Loss Prevention (DLP), encryption, redaction, or other strategies for protecting PII and sensitive data.
Good understanding of processor architecture and ability to work with providers to map use cases to appropriate platforms.
Demonstrated comfort using AI-assisted tools professionally and treating AI as a standard part of modern workflow.
Bachelor’s degree in computer science, information technology, information systems, or a related discipline; equivalent experience and/or alternative qualifications will be considered.
Willingness to travel 10%–20%.

Nice To Haves

Advanced degree in computer science, data science, AI, or a related field.
Experience with big data technologies such as Hadoop or Spark.
Experience with reinforcement learning, generative AI, or autonomous systems.
Knowledge of MLOps practices and tools.
Knowledge of insurance-specific AI applications.
Hands-on experience working with AI/ML as a data scientist.
Experience deploying machine learning models in production environments.
Understanding of distributed systems and microservices architecture.

Responsibilities

Design and develop scalable AI platforms for machine learning and deep learning models.
Collaborate with data scientists to understand model requirements and optimize infrastructure.
Implement and maintain data pipelines, model deployment strategies, and monitoring systems.
Troubleshoot and tune performance to ensure AI services are highly available and reliable.
Develop and maintain systems primarily using Python, R, or Java, including REST APIs and small, secure web front ends.
Design and implement schemas and queries for relational, NoSQL, and graph databases.
Integrate with third-party services, including Kafka and Entra ID.
Collaborate with frontend teams to integrate frontend and backend systems.
Ensure backend systems are scalable, reliable, and secure.
Troubleshoot and debug issues, write optimized code, and develop solutions to complex problems.
Stay current with AI and machine learning advancements and incorporate relevant developments into the platform.
Automate repetitive tasks and improve platform efficiency using scripting and configuration management tools.
Provide technical guidance and support to other teams on AI platform usage and best practices.
Document platform architecture, workflows, and procedures.
Use AI-assisted tools as part of daily engineering work.