Generative AI Engineer

What we're looking for

• Customer Deployment & Integration
• Work directly with enterprise clients to architect, build, and deploy AI solutions that integrate into their existing creative and business workflows.
• Design and optimize large-scale AI pipelines (e.g., data ingestion, model deployment, fine-tuning, inference optimization).
• Develop proof-of-concepts and scalable production systems that demonstrate the power of generative AI.
• Collaborate & Innovate: work with Technical Architects, Engagement Managers, and Product teams to define customer requirements/use-cases, run technical workshops, co-create GenAI solutions.
• Prototype Rapidly: build proof-of-concepts in days, iterate based on feedback, drive quick wins. UX/UI and prototyping skills are helpful.
• Engineer End-to-End: design, build, and deploy full-stack applications / microservices integrating Firefly APIs, extensibility platforms, headless CMS, etc.
• Bridge to Product: capture field-proven use cases and feed them back into the product & engineering roadmaps.
• Automate & Scale: develop reusable components, CI/CD pipelines, governance & best practices for repeatable delivery.
• Operate at Speed: work in fast-paced, evolving environments; own delivery sprints; adapt to changing trends.
• Documentation / Knowledge Sharing: share playbooks, prompt patterns, internal tooling, etc.
• Strategic Advisory
• Translate customer business objectives into technical AI strategies and roadmaps.
• Advise customers on how to adapt processes, governance, and adoption models for AI-driven outcomes.
• Identify opportunities where AI can unlock new business models, workflows, or creative capabilities.
• Engineering Excellence
• Customize and extend foundation models through fine-tuning, prompt engineering, and domain-specific adaptation.
• Build APIs, SDKs, and integration layers to connect AI systems with enterprise applications.
• Ensure solutions meet enterprise standards for performance, reliability, security, and compliance.
• Collaboration & Evangelism
• Partner with product and research teams to provide customer feedback that informs model development and platform strategy.
• Mentor client and partner engineers to accelerate adoption of AI technologies.
• Act as a thought leader in customer workshops, executive briefings, and industry discussions.

Required:

• 10+ years of experience in software engineering, machine learning engineering, or applied AI roles.
• Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow .
• Strong understanding of large language models (LLMs) , multimodal generative AI , and fine-tuning techniques .
• Experience building scalable APIs, pipelines, and distributed systems for real-world AI deployments.
• Excellent communication skills, with the ability to explain complex AI concepts to both technical and non-technical stakeholders.
• Demonstrated success in customer-facing or forward-deployed engineering roles .

• Full-Stack Experience: 3+ years in building and launching production software; familiarity with front-end (React.js, Next.js, Angular) and back-end (Node.js, Java / Spring Boot), and REST/GraphQL, HTML/JS etc.
• GenAI Mastery: experience with large language models (LLMs), diffusion models, prompt engineering, RAG pipelines, vector databases, multimodal AI (text, image, video, audio) etc.
• Cloud & DevOps / Infrastructure: comfort with AWS / Azure / cloud compute, containerization (Docker, Kubernetes), serverless, CI/CD, infrastructure as code, monitoring/logging etc.
• Adobe Platform Fluency: understanding of Firefly APIs/services, Creative Cloud SDK/APIs, Experience Cloud integrations are nice to have.
• Strong Communication & Customer Centricity: ability to translate technical details to non-technical stakeholders and work with customers.
• Ability to thrive in ambiguous / fast-changing environments ("startup DNA").
• Track record of enterprise consulting, solution architecture, or technical pre-sales .
• Knowledge of responsible AI practices , including model evaluation, bias mitigation, and compliance.
• Entrepreneurial mindset with the ability to thrive in ambiguous, fast-moving environments—identifying opportunities, driving solutions end-to-end, and innovating beyond defined playbooks.
• Master’s in Computer Science, AI/ML, or related field.

What Success Looks Like

• Customers achieve measurable outcomes (efficiency, creativity, revenue impact) from deployed AI solutions.
• AI adoption is accelerated through seamless integration with customer workflows.
• Strong partnerships are formed with product, research, and business leaders to continuously advance the AI platform.
• You are seen by customers not just as an engineer, but as a strategic advisor driving AI transformation .