Associate AI Engineer

About the Role

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. This role offers two areas of specialization within a common AI Engineering career framework:

• AI Engineering Path: Build, test, and deploy AI-powered applications, generative AI solutions, intelligent agents, and machine learning capabilities.
• Data Engineering & Analytics Path: 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.

Key Responsibilities

• 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

• Bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, Mathematics, Statistics, or a related discipline, or equivalent practical experience.
• 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.

Key Skills

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