Early Career Consult Program - Associate AI Engineer

The Associate AI Engineer is an entry-level technical role supporting the design, development, deployment, and optimization of artificial intelligence, machine learning, analytics, and data-driven solutions. Working alongside experienced AI Engineers, Data Engineers, Data Scientists, Solution Architects, and business stakeholders, you will develop foundational skills in modern AI technologies, cloud platforms, software engineering, data engineering, analytics, and responsible AI practices. Main focus is on the Agentic Ai path/relevance. Join Kyndryl and gain the trust, autonomy and teamwork to help define what comes next. This role offers two areas of specialization within a common AI Engineering career framework: Build, test, and deploy AI-powered applications, generative AI solutions, intelligent agents, and machine learning capabilities that help solve business challenges and improve user experiences. Prepare, manage, and optimize the data foundations that power AI, machine learning, analytics, and business intelligence solutions. Candidates may begin in one focus area but will gain exposure to both disciplines as they develop their careers. What You'll Do 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. 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. 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. Who You Are Required skills and experience 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. 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 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. Key Skills Generative AI and large language model fundamentals Intelligent agents and automation Prompt engineering and AI solution testing APIs and system integration Machine learning concepts SQL and data querying Data preparation and transformation Data quality and validation Exploratory data analysis Data pipelines and workflows Analytics and business insights generation What Success Looks Like As an Associate AI Engineer, you will: Deliver high-quality work with increasing independence and technical proficiency. Develop practical AI, software, and data engineering capabilities. Contribute to solutions that improve business outcomes and user experiences. Demonstrate sound judgment, curiosity, and continuous learning. Collaborate effectively across multidisciplinary teams. Consistently apply security, governance, privacy, and responsible AI practices in your work #J-18808-Ljbffr Kyndryl