Job description

CleanSlate Technology Group (CSTG) is a modern technology consulting firm transforming how organizations use Data, AI, and Cloud Modernization to fuel innovation. As a top-tier cloud partner, we design and deliver AI solutions and modern data platforms that unlock the power of data, accelerate transformation, and enable businesses to build what's next. We help clients accelerate their AI journey with security and governance built on a strong data foundation. With an operational mindset, we break through legacy limitations, simplify complexity, and create future-ready technology foundations that inspire new possibilities. At CSTG, you'll be part of a team that empowers organizations to innovate faster, make smarter decisions, and compete boldly in a rapidly evolving digital world. Through our Data and AI Practice, we partner with clients to build scalable, reliable data foundations across AWS and Azure. Our focus is on modern data engineering, developing data platforms and pipelines that enable analytics, machine learning, and AI in real world production environments. We believe strong data engineering is critical to making AI useful, trustworthy, and sustainable. CSTG consultants combine proven engineering practices with a hands-on consulting mindset, helping clients move from strategy to implementation while delivering solutions aligned to real business needs. What does your team look like: Our teams bring together CSTG consultants and client stakeholders to solve complex data and AI challenges. As an Associate AI/ML Engineer, you will collaborate with AI/ML, data, and application engineers, developing your primary skills in AI and machine learning while learning how data platforms and applications support reliable, production-ready solutions. We value curiosity, practical problem-solving, and a willingness to learn from others. What does your role look like: As an Associate AI/ML Engineer in CSTG’s Data and AI Practice, you will support client delivery by completing well-defined development, testing, and documentation tasks with guidance from experienced engineers. Your work will contribute to AI and machine learning solutions, including generative AI and agent-based use cases, that become part of client production systems. Associate AI/ML Engineers apply established techniques and learn AI and ML engineering best practices through assigned project work, code reviews, and feedback from experienced team members. You will communicate progress, ask questions, and raise blockers within the delivery team. As you build familiarity with the work, you will participate in client discussions with support from more experienced consultants. Success in this role means completing assigned work with increasing independence, incorporating feedback, documenting results, and building sound engineering habits. Your focus will be on reliable contributions to team delivery while developing experience with real-world AI solutions. Responsibilities: • Development and integration: Complete assigned development tasks for client AI and machine learning solutions, including generative AI and agent-based capabilities, using established approaches and guidance from experienced engineers. • Build and test defined components of AI workflows, services, and integrations alongside data and application platforms, following team designs and review processes. • Apply established AI techniques, patterns, and tools to solve well-defined problems. • Testing and evaluation: Run established tests and evaluation procedures, document results and model limitations, and raise unexpected outputs or data quality concerns for review. • Participate in design discussions, reviews, and team feedback sessions. • Production delivery support: Collaborate with data and application engineers on assigned tasks that help prepare AI solutions for production, following established testing, deployment, and review procedures. • Responsible AI: Follow project guidance for data usage, bias, transparency, and privacy. Document concerns and seek guidance before using data or sharing outputs when requirements are unclear. • Code quality and documentation: Write clear, maintainable code for assigned tasks, participate in code reviews, incorporate feedback, and document implementation details and test results. • Continue developing skills in AI, machine learning, and production delivery practices. Requirements • Foundational hands-on experience building or supporting AI or machine learning solutions through professional work, internships, coursework, research, or personal projects. • Foundational Python skills, including the ability to write, read, and debug simple code for data preparation or AI and machine learning tasks; experience may come from work, coursework, or projects. • Familiarity with machine learning, generative AI, or agent-based approaches. • Foundational experience preparing or working with structured or unstructured data for AI use cases, including checking data quality; coursework and project experience are welcome. • Foundational understanding of version control and tooling such as Git • Exposure to deploying solutions across multistage environments (development, test, and production) • Familiarity with AWS, Databricks, or Azure services. • Understanding of foundational data engineering concepts, such as data pipelines and data quality. • Ability to communicate technical concepts clearly within a team and with client stakeholders. Preferred Qualifications: • Exposure to LLM platforms or tools such as Amazon Bedrock, Azure AI Foundry, or similar. • Agentic tooling like AWS Quick/Claude/Copilot/OpenClaw/Dots/Grok Bot. • Exposure to Agentic development with tooling like Claude Code/Kiro/Cursor. • Exposure to MLOps practices, including model versioning or monitoring. • Experience working alongside data engineering teams on shared solutions. • AWS, Azure, or entry-level AI/ML certifications. • Databricks or Snowflake certifications or willingness to obtain them. • Claude certifications or a willingness to obtain them.

Requirements

Must-haves

CSTG consultants combine proven engineering practices with a hands-on consulting mindset, helping clients move from strategy to implementation while delivering solutions aligned to real business needs.
Build and test defined components of AI workflows, services, and integrations alongside data and application platforms, following team designs and review processes.
Apply established AI techniques, patterns, and tools to solve well-defined problems.
Testing and evaluation: Run established tests and evaluation procedures, document results and model limitations, and raise unexpected outputs or data quality concerns for review.
Participate in design discussions, reviews, and team feedback sessions.
Production delivery support: Collaborate with data and application engineers on assigned tasks that help prepare AI solutions for production, following established testing, deployment, and review procedures.
Responsible AI: Follow project guidance for data usage, bias, transparency, and privacy. Document concerns and seek guidance before using data or sharing outputs when requirements are unclear.
Code quality and documentation: Write clear, maintainable code for assigned tasks, participate in code reviews, incorporate feedback, and document implementation details and test results.
Continue developing skills in AI, machine learning, and production delivery practices.
Foundational hands-on experience building or supporting AI or machine learning solutions through professional work, internships, coursework, research, or personal projects.
Foundational Python skills, including the ability to write, read, and debug simple code for data preparation or AI and machine learning tasks; experience may come from work, coursework, or projects.
Familiarity with machine learning, generative AI, or agent-based approaches.
Foundational experience preparing or working with structured or unstructured data for AI use cases, including checking data quality; coursework and project experience are welcome.
Foundational understanding of version control and tooling such as Git
Exposure to deploying solutions across multistage environments (development, test, and production)
Familiarity with AWS, Databricks, or Azure services.

Nice-to-haves

Exposure to LLM platforms or tools such as Amazon Bedrock, Azure AI Foundry, or similar.
Agentic tooling like AWS Quick/Claude/Copilot/OpenClaw/Dots/Grok Bot.
Exposure to Agentic development with tooling like Claude Code/Kiro/Cursor.
Exposure to MLOps practices, including model versioning or monitoring.
Experience working alongside data engineering teams on shared solutions.
AWS, Azure, or entry-level AI/ML certifications.
Databricks or Snowflake certifications or willingness to obtain them.
Claude certifications or a willingness to obtain them.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiawsazuremachine learningmlpythonllm

Platform: paylocity
Effort: medium