AI and ML Solution Architect
About The Position
The AI and ML Solution Architect develops and programs integrated software algorithms to structure, analyze, and leverage structured and unstructured data in products and systems. The role uses machine learning and statistical modeling to improve performance, data management, quality, and accuracy, and translates technical specifications and customer requirements into AI/ML products, solutions, and system improvements. It applies and scales deep-learning technologies, documents installation and maintenance procedures, completes programming, testing, and debugging, and defines and monitors performance metrics. This subject-matter expert solves complex business and technical issues, partners with functional and technical project teams, and exercises independent judgment in determining how to meet objectives. The role may provide technical leadership and mentoring, and contributes to cross-functional initiatives. The position researches emerging AI and machine-learning technologies, designs scalable and robust solutions with cross-functional teams, develops and optimizes models, and deploys and integrates them into production environments. It monitors model performance, analyzes metrics, and improves models and systems. It also leads design reviews, helps establish design and implementation standards, mentors team members, leads collaborative meetings, addresses challenges, and presents strategic recommendations to senior stakeholders.
Requirements
- Bachelor's or master's degree in computer science, engineering, data science, machine learning, artificial intelligence, or a closely related quantitative discipline.
- Typically 7–10 years of experience.
- Deep understanding of machine-learning algorithms, including linear regression, decision trees, support vector machines, random forests, neural networks, and reinforcement learning.
- Proficiency in model selection, hyperparameter tuning, and evaluating model performance using appropriate metrics.
- Strong foundation in mathematics and statistics, including linear algebra, calculus, probability theory, and statistical concepts.
- Ability to understand and develop complex machine-learning models and algorithms.
- Strong programming skills in Python, R, or Java.
- Experience developing production-level code and familiarity with software-engineering best practices, version control systems such as Git, and software-development methodologies.
- Advanced knowledge and experience in deep learning, including neural-network architectures such as convolutional neural networks, recurrent neural networks, and transformers, as well as transfer learning, generative models, and deep-learning optimization algorithms.
- Experience conducting research, exploring emerging technologies, and identifying opportunities to apply state-of-the-art techniques to complex problems.
- Excellent communication skills for effective collaboration with cross-functional teams and stakeholders.
- Strong problem-solving and critical-thinking abilities to guide projects, make strategic decisions, and resolve complex technical challenges.
- Experience with popular machine-learning frameworks and libraries such as TensorFlow, PyTorch, or scikit-learn.
Nice To Haves
- Knowledge of libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, and Keras.
- A deep understanding of statistical modeling, data mining, and data visualization.
Responsibilities
- Research and stay current with advancements in AI and machine-learning technologies, frameworks, and algorithms; experiment with techniques to solve complex problems and improve models.
- Collaborate with cross-functional teams to understand business requirements and design AI and machine-learning solutions.
- Select appropriate algorithms, models, and frameworks and architect systems for scalability, efficiency, and robustness.
- Develop, implement, and optimize machine-learning models, including data preprocessing, feature engineering, model selection, hyperparameter tuning, and training on large datasets.
- Monitor and improve model performance and accuracy.
- Deploy machine-learning models into production, accounting for scalability, performance, and security, and integrate them with existing software systems and infrastructure.
- Collect and analyze metrics from deployed models, identify areas for improvement, and fine-tune models and algorithms to enhance system performance.
- Organize and lead design review sessions and guide discussions to align with project requirements and best practices.
- Mentor junior and mid-level team members and provide technical leadership, guidance, and feedback.
- Work with the engineering manager and team lead to establish design and implementation standards and support continuous improvement and project alignment.
- Lead meetings and foster a collaborative, productive team environment.
- Proactively address and resolve challenges.
- Develop and deliver strategic presentations and reports to senior stakeholders, providing technical and business insights and recommendations.
- Apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from datasets.
Benefits
- Comprehensive benefits supporting physical, financial, and emotional wellbeing for employees and their loved ones.
- Career development programs supporting professional growth and career goals.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
