Rockstar is recruiting for a data intelligence platform company focused on security analytics, investigations, fraud detection, and enterprise AI systems. Their team is dedicated to building production AI products that help organizations extract actionable insights from complex data. They are seeking a Jr. AI Engineer to contribute to their growing AI capabilities.

Our client is seeking a Jr. AI Engineer/Jr. Machine Learning Engineer to support the development, testing, and improvement of AI-powered features across their data intelligence platform. This role is designed for an early-career engineer who has strong technical fundamentals, curiosity about GenAI systems, and an interest in learning how production AI products are built and maintained.

The Jr. AI Engineer will work closely with senior engineers to assist with prompt experimentation, data preparation, RAG pipeline support, model evaluation, documentation, debugging, and basic AI service development. This role offers hands-on exposure to LLMs, embeddings, retrieval systems, ML workflows, and production engineering practices.

• Assist in developing AI-powered features using Python, LLM tools, ML libraries, APIs, and internal platform services.
• Support prompt engineering, prompt testing, model comparison, and evaluation of AI-generated outputs.
• Help build and maintain RAG workflows, including document preparation, chunking, metadata tagging, embedding generation, retrieval testing, and result review.
• Prepare, clean, format, and validate datasets used for model testing, prompt evaluation, and AI experiments.
• Assist with model and workflow evaluation by reviewing outputs, identifying errors, documenting patterns, and comparing performance across approaches.
• Write clean, readable Python code for scripts, internal tools, prototypes, experiments, and service components.
• Support debugging of AI workflows, data pipelines, API integrations, and model behavior under the guidance of senior engineers.
• Participate in code reviews, design discussions, team planning, and documentation efforts.
• Learn and apply production engineering practices, including Git workflows, testing, logging, Docker, CI/CD, and deployment basics.
• Document experiments, implementation details, findings, and recommendations clearly for technical team members.

• 0–2 years of experience in AI engineering, machine learning, software engineering, data science, or a related technical area.
• Internship experience, academic work, bootcamp projects, portfolio projects, or open-source contributions are acceptable.
• Solid Python programming skills.
• Foundational understanding of machine learning, deep learning, NLP, data processing, and model evaluation concepts.
• Familiarity with tools or libraries such as PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, pandas, NumPy, or similar technologies.
• Interest in LLMs, GenAI systems, prompt engineering, embeddings, semantic search, RAG, and AI agents.
• Ability to work with structured and unstructured data.
• Comfort using Git, notebooks, command-line tools, APIs, and collaborative development workflows.
• Strong attention to detail, curiosity, problem-solving ability, and willingness to learn from feedback.
• Clear written communication skills for documenting technical work and experiment results.

• Portfolio, academic, internship, or project experience involving LLMs, chatbots, semantic search, classification, summarization, automation, or ML workflows.
• Exposure to vector databases, embeddings, document processing, information retrieval, or search systems.
• Familiarity with Docker, cloud environments, CI/CD concepts, or basic deployment workflows.
• Exposure to agent frameworks such as LangGraph, AutoGen, CrewAI, or similar tools.
• Coursework or practical experience in machine learning, NLP, statistics, data engineering, computer science, or software engineering.
• Interest in security analytics, investigations, data intelligence, fraud detection, or enterprise AI systems.

• Foundational knowledge of machine learning, deep learning, NLP, LLMs, prompt engineering, and RAG concepts.
• Solid Python skills with exposure to ML libraries such as PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, or similar tools.
• Experience through coursework, internships, projects, or portfolio work involving AI, data preparation, model testing, search, or automation.
• Ability to document experiments, compare model outputs, support debugging, and learn production ML practices such as Git, APIs, Docker, and CI/CD.

Success in this role will be measured by consistent contribution to AI experiments, clean and reliable implementation work, clear documentation, improved evaluation support, effective debugging assistance, and steady growth in production AI engineering skills. The role should help increase team capacity while developing strong internal AI engineering talent over time.