AI-Driven Operations Engineer

Adobe

San Jose (CA)

On-site

USD 159,200 - 301,600

Full time

14 days+

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Job summary

Adobe seeks an experienced AI/ML software engineer to lead the design and rollout of AI agents and automation tools within C & P Operations. You will identify high‑impact opportunities, prototype solutions, and collaborate with operations, engineering, and analytics to translate business needs into scalable internal applications.

The role emphasizes practical governance, secure deployments, and building maintainable, well‑documented code that can scale across Adobe.

Qualifications

5+ years of software development experience with strong CS fundamentals.
Background in AI/ML, computer science, or related technical field.
Experience with large language models and modern AI frameworks.
Proven ability to develop AI-driven applications and automation.
Proficient in Python or similar languages; experience with APIs, MCPs, and integrations.
Strong software engineering practices: clean code, version control (Git), testing, maintainability.
Experience building production systems beyond prototypes.
Data handling: querying, transforming, and visualizing metrics.
Familiarity with CI/CD and enterprise deployments.
Awareness of security considerations when building AI tools.
Strong problem-solving and curiosity.
Ability to move quickly from concept to prototype and iterate with feedback.
Effective communicator bridging technical and business perspectives, including with senior leadership.
Ability to operate independently in ambiguous environments and drive outcomes with limited direction.

Responsibilities

Identify and prioritize high-impact AI opportunities across C & P Operations.
Design and roll out AI agents and automation tools to boost efficiency.
Collaborate across operations, engineering, and ATS teams to translate needs into scalable solutions.
Prototype, test, and iterate AI-powered tools using LLMs and agent architectures.
Integrate new solutions with Adobe-wide systems for coherence.
Build scalable, well-documented solutions transferable beyond initial context.
Establish guidelines for responsible AI usage and governance.
Drive adoption with high-value user-centric tools.
Monitor performance and continuously improve deployed solutions with analytics teams.
Ensure security considerations in AI tool deployment.