As a Senior Staff Software Engineer focused on Generative AI initiatives, you will provide technical leadership in identifying, implementing, and integrating Generative AI solutions to solve complex business problems. You will bridge the gap between traditional software engineering and AI technologies, leading teams to deliver AI-enhanced features and capabilities while mentoring engineers on practical AI adoption within existing software systems.

• Build and apply custom Generative AI techniques to solve complex business problems by identifying opportunities where AI can enhance existing software systems and create new intelligent features.
• Integrate Generative AI models and capabilities into production software applications, bridging the gap between Generative AI research and practical software engineering implementation.
• Evaluate and select appropriate Generative AI models, tools, and techniques for specific use cases, considering factors like performance, cost, maintainability, and business impact.
• Write high-quality code that combines traditional software engineering with AI components, ensuring robust integration, proper error handling, and comprehensive test coverage.
• Review and translate product requirements into technical solutions that leverage AI effectively, designing systems that seamlessly blend AI capabilities with core application functionality.
• Lead design and code reviews for AI-enhanced applications, ensuring best practices in both software engineering and AI implementation while maintaining Mercury standards.
• Lead multiple sprint teams and projects simultaneously to deliver AI-powered features and improvements, addressing technical challenges in integrating AI into existing software systems.
• Collaborate with product managers, data scientists, and other engineering teams to identify AI opportunities and translate them into practical software solutions.
• Mentor engineers on how to effectively incorporate AI into their software development practices, promoting understanding of AI capabilities and limitations.
• Drive practical AI adoption across teams by demonstrating how to effectively combine software engineering principles with AI technologies as well as presenting to internal tech forums to create awareness and promote education.
• Collaborate with product managers to estimate effort for AI-enhanced features, prioritize AI integration opportunities, and deliver measurable business value through AI implementation.

Minimum:

Bachelor's degree in Computer Science, Software Engineering, or related technical field

Preferred:

Master’s degree or PhD in Computer Science, Software Engineering, or related technical field

Experience:

Minimum:

·       10+ years of software engineering experience with proven experience in cutting-edge technologies and/or those used at Mercury

·       5+ years hands-on experience leading large, multi-team engineering initiatives for implementing complex distributed application systems

Preferred:

·       10+ years working at the intersection of Software Engineering and ML/AI Engineering

·       5+ years working on ML/AI related initiatives within engineering organizations including deploying solutions to production, evaluating ML/AI solutions, custom model training, AI/ML system design, and leveraging both closed and open source AI models

Knowledge and Skills:

• Proficient in programming in one or more industry-standard languages (e.g., Java, Python, Go, C++, JavaScript).

• Proficient in leveraging various data structures, algorithms, and design patterns. Experienced in relational databases, NoSQL, and caching solutions.

• Skilled in APIs using modern protocols like REST and gRPC.

• Experienced in message queuing, data streaming, workflow, and integration frameworks (such as Apache Kafka, Apache NiFi, Amazon SQS, MS MQ, Apache Camel, etc.). Knowledgeable in microservices architecture.

• Experienced in containerization technologies like Docker and Kubernetes. Proficient in cloud platforms such as AWS, Google Cloud, or Azure.

• Experienced in leveraging DevSecOps to improve engineering efficiency.

• Proficient in designing and implementing high-quality distributed application systems that are modular, configurable, maximize reuse, and are fast, effective, user-friendly, secure, compliant, scalable, and maintainable.

• Experienced in leveraging analytics data and experimentation to design and improve systems for speed, scalability, and effectiveness.

• Excellent problem-solving skills leading to innovative solutions.

• Strong communication skills. Strong ability to collaborate and influence cross-functional teams and stakeholders across multiple functional areas to address impediments and deliver desired results.

• Demonstrated leadership skills through mentoring and providing technical guidance to multiple teams within a functional area.

• Experienced in championing operational excellence and best practices with a focus on quality, performance, scalability, maintainability, and observability.

• Experienced in driving technical strategy and vision for multiple teams.