Machine Learning Engineer 5, Senior Manager - Intelligent Foundations and Experiences (IFX)

About the job

Responsibilities

• Design, build, and/or deliver ML models and components to solve business problems in collaboration with Product and Data Science teams.
• Build and scale multi-tenant platforms for large-scale ML model training and serving.
• Inform ML infrastructure decisions using knowledge of ML modeling techniques, including model choice, data and feature selection, training, hyperparameter tuning, and bias/variance.
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
• Collaborate within a cross-functional Agile team to create and enhance software for big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage or build cloud-based architectures and platforms for optimized ML model delivery at scale.
• Construct optimized data pipelines to feed ML models.
• Apply continuous integration and continuous deployment best practices, including test automation and monitoring, for successful deployment of ML models and application code.
• Ensure code is well-managed for reduced vulnerabilities, models are governed for risk, and ML practices follow Responsible and Explainable AI guidelines.
• Use programming languages like Python, Scala, or Java.

Requirements

• Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field.
• At least 6 years of experience programming with Python, Java, Golang, or C++.
• At least 6 years of Machine Learning experience using industry-standard frameworks like PyTorch or Tensorflow and libraries such as Pandas, NumPy, Scikit-learn.
• At least 6 years of experience using and operating large-scale distributed systems (Spark, Ray) for AI or Machine Learning data preparation.
• At least 4 years of experience deploying and operating Machine Learning solutions in production and production services in the cloud (AWS, GCP, Azure).
• At least 4 years of experience using Kubernetes to manage large-scale containerized Machine Learning software systems.
• Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field (preferred).
• +2 years experience building production RAG pipelines (chunking, embeddings, vector indexing, graph RAG, or fine-tuning) (preferred).
• +2 years experience leveraging Agentic patterns (Tool Calling, ReAct loops, Plan-and-Execute, Multi-Agent Collaboration) (preferred).
• +2 years with LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel) and model APIs (OpenAI, Anthropic, Bedrock, Ollama, Hugging Face) (preferred).
• +2 year experience in building agentic AI systems and agentic workflows (preferred).
• +2 year experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, CUDA, or Golang (preferred).
• +2 years Experience right-sizing models, instance counts, and hardware types given requirements (e.g., context length, token inputs, token outputs) (preferred).
• 5+ years of experience optimizing ML algorithms, configurations, and infrastructure (preferred).
• 5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. (preferred).
• 5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response planning (preferred).
• 5+ years of experience working with Machine Learning techniques, model types, model architectures, training concepts, and model evaluation (preferred).
• 5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models (preferred).
• Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences (preferred).