Machine Learning Engineer - AI Orchestration

Requirements:

• Master's or PhD in Computer Science, Machine Learning, AI, or related field, and/or 3-5 years of relevant ML engineering experience.

• Strong proficiency in Python and deep learning frameworks (PyTorch, TensorFlow).

• Expert-level knowledge of LLM architectures, fine-tuning techniques, and prompt engineering strategies.

• Hands-on experience with LLM frameworks and tools (e.g., OpenAI APIs, Anthropic Claude, LangChain etc.).

• Proven track record of building and deploying ML models in production environments.

• Strong understanding of ML fundamentals including model evaluation, feature engineering, and experiment design.

• Experience with RAG pipelines, vector databases, and semantic search.

• Proficiency in MLOps tools and practices (MLflow, Weights & Biases, Kubeflow, model serving frameworks).

• Experience with cloud ML platforms (AWS SageMaker, Azure ML).

• Excellent analytical and problem-solving skills with ability to interpret model behavior and debug complex systems.

• Excellent communication skills, both verbal and written, with ability to explain complex ML concepts to non-technical partners.

• Proficiency in English, both written and spoken.

• Familiarity with Agile/Scrum project management methodologies.

Nice to Have:

• Publications or contributions to ML/AI research.

• Knowledge of responsible AI, model interpretability, and bias mitigation techniques.

• Experience with agent-based simulation and planning algorithms.

• Experience with distributed training and large-scale model fine-tuning.

What You'll Be Doing:

• Design, develop, and deploy machine learning models that power Adobe's Agent Orchestrator Platform, enabling intelligent decision-making and autonomous agent behaviors in a large-scale, multi-cloud environment.

• Research and implement modern techniques in LLM fine-tuning, prompt engineering, and retrieval-augmented generation to optimize agent performance.

• Build and maintain ML pipelines for model training, evaluation, deployment, and monitoring, ensuring reliability and scalability.

• Conduct experiments and A/B tests to evaluate model performance, measure business impact, and continuously improve agent intelligence.

• Implement MLOps guidelines including model versioning, monitoring, retraining pipelines, and performance tracking.

• Mentor team members on ML guidelines, model optimization techniques, and responsible AI principles.

• Stay ahead with the latest research in LLMs and agent-based AI, applying innovations to production systems.

• Drive ML projects from conception to production with clarity and precision, maintaining strong ownership and direction.

Perks and Benefits:

• Company committed to outstanding employee experiences and equal opportunity.

• Access to the latest research and innovation in LLMs and agent-based AI.

• Possibility to influence digital interactions on a global scale with Adobe's solutions.

• Engaging work environment focused on creativity and powerful digital experiences.