Principal Machine Learning Engineer

Overview:
The primary mission of the Principal Machine Learning Engineer role is to build a scalable foundation for machine learning and data science across Lennar. The position sits in our Emerging Technologies and Advanced Analytics Solutions team, which aims to disrupt our industry with emerging technologies and data science solutions that drive sustainable competitive advantage for Lennar. The Principal Machine Learning Engineer will architect, design, and leaad the build and integration of our data science and AI platforms and solutions that will serve as the backbone for all machine learning applications at Lennar.

Responsibilities:
Define ML Architecture Strategy: In partnership with Enterprise Architecture, define and drive the overall machine learning architecture strategy, ensuring alignment with business goals and technology landscape.

· Design and Architect ML Solutions: Collaborate with data scientists, engineers, and stakeholders to architect end-to-end machine learning infrastructure, including data ingestion, preprocessing, model development, deployment, and monitoring.

· Technology Assessment and Selection: Evaluate and select appropriate ML frameworks, libraries, and tools based on project requirements, scalability, and performance. Evaluate and integrate automated machine learning (AutoML) solutions and tools into the machine learning architecture, leveraging their capabilities to streamline model development, hyperparameter tuning, and feature engineering processes.

· Scalable and Reliable Infrastructure: Design highly scalable, distributed, and fault-tolerant model training and deployment infrastructure to handle large volumes of data and real-time inference.

· Model Evaluation and Monitoring: Evaluate model performance, conduct rigorous testing, and iterate models to achieve optimal

results. Develop methodologies for evaluating and selecting the most appropriate machine learning models, considering trade-offs between accuracy, complexity, interpretability, and scalability. Monitor models for drift continuously and build solution for re-training.

· Performance Optimization: Continuously optimize the performance of machine learning models, data pipelines, and infrastructure to achieve maximum efficiency and scalability.

· Mentoring and Knowledge Sharing: Mentor junior machine learning engineers, provide guidance, and promote a culture of knowledge sharing and continuous learning within the team.

· Collaboration and Leadership: Collaborate with cross-functional teams, provide technical leadership, and guide junior machine learning engineers. Collaborate with data scientists, engineering teams, and stakeholders to understand business requirements, provide guidance on the applicability of autoML and generative AI

· Documentation and Reporting: Document design decisions, code, and processes accurately and thoroughly. Prepare reports and presentations to communicate findings and recommendations to stakeholders.

· Ensure Data Quality and Governance: Establish processes and guidelines for data quality, validation, and governance to ensure the reliability and integrity of data used for training and inference.

· Security and Privacy: Implement robust security and privacy measures to protect sensitive data and ensure compliance with relevant regulations.

· Stay Abreast of Industry Trends: Stay updated with the latest advancements in machine learning, architectural patterns, industry trends, and emerging technologies to drive innovation and provide strategic guidance

Qualifications:
 Bachelor's or master's degree in computer science, engineering, or a related field. A Ph.D. in a relevant field is preferred.

Extensive ML and Architectural Experience: 10+ years of experience in machine learning, data engineering, or related roles, with a focus on architecting and delivering machine learning solutions at scale.

Experience with cloud-based ML platforms (e.g., AWS, Google Cloud Platform, Azure) is required.

Deep Understanding of ML and AI: In-depth knowledge of various machine learning algorithms, deep learning and/or generative AI architectures, supervised and unsupervised learning techniques, reinforcement/online learning, and their practical applications. Proficiency with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.

Architecture and Design Skills: Strong architectural skills with experience in designing and implementing scalable, distributed, and fault-tolerant ML systems. Familiarity with microservices architecture, containerization (e.g., Docker), and orchestration frameworks (e.g., Kubernetes) is a plus.

Programming and Tooling: Expertise in programming languages like Python, Java, or Scala, and knowledge of ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).

Big Data Technologies: Proficiency with big data technologies like Hadoop, Spark, or similar distributed computing frameworks. Familiarity with data streaming and real-time processing is desirable.

Data Engineering and Preprocessing: Strong understanding of data engineering principles, data preprocessing techniques, data governance, and data integration.

Strong Communication and Leadership: Excellent communication skills with the ability to communicate complex technical concepts to both technical and non-technical stakeholders . Proven leadership abilities with experience in guiding and mentoring engineering teams. Strong team player with the ability to collaborate effectively in cross-functional teams.

Problem-Solving and Analytical Skills: Strong analytical thinking, problem-solving, and troubleshooting skills to address complex ML and architectural challenges.

Adaptability and Innovation: Ability to adapt to evolving technologies and business needs. Strong drive for innovation and continuous learning.

Generative AI: Experience developing generative AI applications and deploying in a business setting is highly desirable

Model Development and Evaluation: Proven experience in developing and evaluating machine learning models, including model selection, hyperparameter tuning, and performance evaluation techniques.

Strong Mathematical and Statistical Background: In-depth knowledge of mathematical and statistical concepts relevant to machine learning, such as linear algebra, probability theory, and statistical inference.

Software Engineering Skills: Familiarity with software engineering best practices, version control systems, and agile development methodologies.

Technologies and Tools: In addition to typical data and software engineering toolsets, the candidate should have experience broad experience across the ML toolset:

Machine Learning Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras, Caffe, MXNet

Model Deployment: TensorFlow Serving, Flask, Docker containers

Integration with AutoML: Google AutoML, H2O.ai, DataRobot

Model Interpretability and Explainability: SHAP, Lime, ELI5

Natural Language Processing (NLP): NLTK, SpaCy, Gensim, BERT

Reinforcement Learning: OpenAI Gym, Stable Baselines

Generative Adversarial Networks (GANs): TensorFlow-GAN, PyTorch-GAN, Pix2Pix, CycleGAN

Neural Architecture Search: AutoKeras, Google Neural Architecture Search (NAS)

Type:
Regular Full-Time

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