Associate AI Engineer

About the Role

The Associate AI Engineer is an entry-level technical role at Kyndryl, supporting the design, development, deployment, and optimization of artificial intelligence, machine learning, analytics, and data-driven solutions. This role offers two areas of specialization within a common AI Engineering career framework: AI Engineering Path and Data Engineering & Analytics Path. Applicants must be legally authorized to work in the United States without restriction and be able to work at the Frisco office 5 days per week.

Key Responsibilities

Core Responsibilities (All Associates)

• Collaborate with technical teams and business stakeholders to understand requirements and support AI-enabled business outcomes.
• Contribute to the design, development, testing, deployment, and support of AI and data solutions.
• Participate in agile development practices, code reviews, troubleshooting, and continuous improvement activities.
• Document technical solutions, data sources, workflows, assumptions, and operational procedures.
• Apply security, governance, privacy, compliance, and responsible AI standards in all work.
• Build expertise in modern cloud, AI, and data technologies through hands-on learning and project experience.

AI Engineering Focus

• Support the development of AI, machine learning, and generative AI applications.
• Assist in building intelligent agents, conversational experiences, retrieval-augmented generation (RAG) solutions, and AI-assisted workflows.
• Develop and maintain application components, APIs, integrations, and automation capabilities.
• Help evaluate model performance, response quality, scalability, and user experience.
• Participate in testing, monitoring, and optimization of AI solutions.
• Learn modern AI frameworks, cloud AI services, and software engineering practices.

Data Engineering & Analytics Focus

• Collect, cleanse, transform, validate, and prepare structured and unstructured data for AI and analytics solutions.
• Use SQL, Python, and approved platform tools to query, manipulate, and analyze data.
• Assist with exploratory data analysis to identify patterns, trends, insights, and data quality issues.
• Support the creation of data workflows, reusable scripts, data pipelines, and data preparation processes.
• Help prepare training data, features, and evaluation datasets for machine learning and AI initiatives.
• Contribute to data governance, quality, lineage, and operational excellence practices.

Requirements & Tech Stack

• Working knowledge of Python or a similar programming language.
• Basic understanding of software development principles and problem-solving techniques.
• Familiarity with data structures, databases, APIs, cloud technologies, or modern development tools.
• Strong analytical, communication, collaboration, and documentation skills.
• Demonstrated curiosity and interest in emerging AI and data technologies.

Preferred Qualifications

• Exposure to Microsoft Azure, Azure AI Services, Azure OpenAI, AWS, Google Cloud, or similar platforms.
• Familiarity with AI, machine learning, generative AI, large language models, retrieval systems, or intelligent automation.
• Exposure to SQL, data analysis, data preparation, ETL/ELT, or data pipeline concepts.
• Experience with Git, CI/CD, Docker, DevOps, MLOps, or software engineering practices.
• Knowledge of visualization, reporting, or business intelligence tools.
• Relevant certifications in AI, cloud, data, or software engineering technologies.
• Understanding of responsible AI, data privacy, security, and governance principles.

Qualifications

• Bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, Mathematics, Statistics, or a related discipline, or equivalent practical experience.
• 0-3 years of experience in software engineering, AI, machine learning, data engineering, analytics, or related technical fields.

Key Skills

Common Skills

• Problem solving and analytical thinking
• Python programming
• Cloud platform fundamentals
• Technical documentation
• Collaboration and communication
• Responsible AI and technology governance

AI Engineering Skills

• Generative AI and large language model fundamentals
• AI application development
• Intelligent agents and automation
• Prompt engineering and AI solution testing
• APIs and system integration
• Machine learning concepts

Data Engineering & Analytics Skills

• SQL and data querying
• Data preparation and transformation
• Data quality and validation
• Exploratory data analysis
• Data pipelines and workflows
• Analytics and business insights generation