Data AI/ML (Artificial Intelligence and Machine Learning) Engineering involves the use of algorithms and statistical models to enable systems to analyze data, learn patterns, and make data-driven predictions or decisions without explicit human programming. AI/ML applications leverage vast amounts of data to identify insights, automate processes, and solve complex problems across a wide range of fields, including healthcare, finance, e-commerce, and more. AI/ML processes transform raw data into actionable intelligence, enabling automation, predictive analytics, and intelligent solutions. Data AI/ML combines advanced statistical modeling, computational power, and data engineering to build intelligent systems that can learn, adapt, and automate decisions.
• Independently design and implement scalable machine learning solutions and data systems, ensuring end to end workflows, large scale analytics and reliability
• Collaborate with stakeholders to translate business needs into data engineering solutions, evaluate user journeys and challenge business requirements to ensure seamless, value driven delivery and integration of solutions
• Implement and refine feature engineering, monitoring, ML pipelines, deploy models in production, and address challenges in data pipelines
• Apply innovative problem-solving techniques, leveraging advanced methodologies to find unique approaches to complex problems and improve outcomes
• Investigate and resolve complex challenges in data models and deployment to ensure reliable solutions that meet performance benchmarks
• Mentor team members through code reviews, pairing sessions, knowledge-sharing sessions, and contribute to Communities of Practice
• Communicate technology, infrastructure, and deployment decisions clearly to both technical and non-technical stakeholders while maintaining detailed documentation to ensure reproducibility, scalability, and understanding
• Ensure readiness for production releases, focusing on testing, monitoring, observability, and maintaining scalability and reusability of models for future projects
• Drive cross-team and cross-discipline initiatives to optimize workflows, remove redundant applications and processes, share best practices, and enhance collaboration between teams
• Demonstrate awareness of shared platform capabilities and actively identify opportunities to leverage them in designing efficient and scalable data engineering solutions
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CORE SKILLS
Programming: Writing code to manipulate, analyze, and visualize data, often using languages like Python, R, and SQL.
Proficiency Level: Proficient
AI & Machine Learning: Creating systems that can perform tasks that typically require human intelligence. Using Machine learning (ML), a subset of AI that uses algorithms to learn from and make predictions based on data
Proficiency Level: Proficient
Data Analysis: Inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making
Proficiency Level: Proficient
Machine Learning Pipelines: Using automated workflows that manage the end-to-end process of training and deploying machine learning models.
Proficiency Level: Advanced
Model Deployment: Making a trained machine learning model available for use in production environments.
Proficiency Level: Advanced
SPECIALIZED SKILLS
Big Data Technologies: Using continuous integration and continuous delivery (CI/CD) pipelines to automate the process of software development, including building, testing, and deploying code
Natural Language Processing (NLP): Focusing on the interaction between computers and humans through natural language.
Data Architecture: Designing and structuring of data systems, ensuring that data is stored, managed, and utilized efficiently
Data Processing Frameworks: Using tools and libraries to process large data sets efficiently, such as Apache Hadoop and Apache Spark.
Technical Documentation: Creating and maintaining documentation that explains the functionality, use, and maintenance of software or systems.
Deep Learning: Using a subset of machine learning involving neural networks with many layers, used to model complex patterns in data.
Statistical Analysis: Collecting and analyzing data to identify patterns and trends, and to make informed decisions.
Data Engineering: Designing and building systems for collecting, storing, and analyzing data at scale.
Definition of Proficiency Levels:
Foundational: This is the entry level of the skill, typically expected when starting a new role or working with the skill for the first time. You rely on strong manager support, coaching, and training as you build the capability to progress to higher proficiency levels.
Proficient: This is the level at which you are considered effective in the skill. You demonstrate more than just functional competence—you begin to have a noticeable impact in your role by applying the skill consistently and meaningfully. You require only minimal support, coaching, or training to apply the skill successfully.
Advanced: This is the level where you move beyond meeting expectations to actively leading, influencing, and delivering considerable impact across the wider business. You are seen as a role model, demonstrate the skill independently, and require little to no manager support.