Lead Machine Learning Engineer - AI Orchestration

Requirements:

• PhD or equivalent experience in Computer Science, Machine Learning, AI, or related field, and/or 5+ years of hands-on ML engineering experience with proven production impact.
• Expert-level proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, JAX) with deep understanding of model architectures and optimization.
• Extensive hands-on experience with modern LLM frameworks and tools (e.g., OpenAI APIs, Anthropic Claude, LangChain etc.).
• Proven track record of architecting, building, and deploying production ML systems that operate at scale with measurable business impact.
• Proficient in analyzing and solving problems, capable of debugging intricate ML systems and describing model behavior.
• Outstanding communication skills, both verbal and written, with the ability to articulate complex ML concepts to technical and non-technical audiences.
• Proficiency in English, both written and spoken.

Nice to Haves:

• Practical knowledge of reinforcement learning, multi-agent systems, and agent-based learning.
• Publications or contributions to ML/AI research.
• Deep knowledge of responsible AI, model interpretability, fairness, and bias mitigation in production systems.
• Experience in developing and guiding high-performing ML teams.

What You'll Be Doing:

• Lead the design, development, and deployment of modern machine learning models that power Adobe's Agent Orchestrator Platform, enabling sophisticated autonomous agent behaviors in a large-scale, multi-cloud environment.
• Drive ML architecture decisions for the platform, establishing guidelines for model development, deployment, and lifecycle management.
• Research and implement innovative techniques in LLM fine-tuning, prompt engineering, retrieval-augmented generation, and multi-agent coordination to push the boundaries of agent intelligence.
• Architect and optimize end-to-end ML pipelines for model training, evaluation, deployment, and monitoring at scale, ensuring production-grade reliability and performance.
• Lead experimentation strategy including A/B testing frameworks, evaluation metrics, and continuous improvement processes to improve business impact.
• Establish and evangelize MLOps standard methodologies including model versioning, automated monitoring, retraining pipelines, and performance optimization across the organization.
• Partner with platform engineers, product managers, and data scientists to integrate ML solutions seamlessly into production systems with stringent latency, availability, and scalability requirements.
• Train and support junior ML engineers, promoting an atmosphere of creativity, precision, and ethical AI approaches.
• Drive complex ML projects from research through production deployment with strategic vision, maintaining strong ownership and delivering measurable business outcomes.

Perks and Benefits:

• Opportunity to work with cutting-edge AI technologies.
• Collaborative and inclusive work environment.
• Competitive compensation package.
• Career development opportunities.
• Health and wellness programs.