Software Engineer AI

Overview

As a 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.

State specific pay scales for this role are as follows:

$105,245 to $199,452 (CA, NJ, NY, WA, HI, AK, MD, CT, RI, MA)

$95,677 to $181,320 (NV, OR, AZ, CO, WY, TX, ND, MN, MO, IL, WI, FL, GA, MI, OH, VA, PA, DE, VT, NH, ME)

$86,110 to $163,118 (UT, , MT, NM, SD, NE, KS, OK, IA, AR, LA, MS, AL, TN, KY, IN, SC, NC, WV)

The expected base salary for this position will vary depending on a number of factors, including relevant experience, skills and location.

• Apply 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 to deliver AI‑powered features and improvements, addressing technical challenges in integrating AI into existing software systems.
• Collaborate with product managers, data scientists, and 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.
• Collaborate with product managers to estimate effort for AI‑enhanced features, prioritize AI integration opportunities, and deliver measurable business value through AI implementation.

Education:

• Minimum:
  Bachelor’s Degree in Computer Science, Information Systems or other related fields Or equivalent combination of education and experience.
• Preferred:
  Master’s Degree in Computer Science.

Experience:

• Minimum: 7+ years software engineering experience with proven experience in cutting‑edge Technologies and/or those used at Mercury. 3+ years hands‑on experience leading multi‑team engineering initiatives for building features in a distributed application systems environment.
• Preferred: 7+ years working at the intersection of Software Engineering and AI Engineering. 3+ years working on Generative AI related initiatives including leveraging closed and open source models and libraries.

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, No
  
  SQL, 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…