Rockstar
security analytics, investigations, fraud detection, and enterprise AI systems
JrAIEngineer
“Jr AI Engineer at Rockstar. Skills: Python, Machine Learning, LLMs, RAG. Develop AI-powered features. Support prompt engineering”
What You'll Achieve.
Consistent contribution to AI experiments; Clean and reliable implementation work; Clear documentation; Improved evaluation support; Effective debugging assistance; Increase team capacity; Develop internal AI engineering talent
Industry & Context.
problem-solving ability
What They're Looking For.
Must Have
0–2 years of experience in AI engineering, machine learning, software engineering, data science, or a related technical area, 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, attention to detail, curiosity, problem-solving ability, and willingness to learn from feedback, Clear written communication skills for documenting technical work and experiment results, 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
Nice to Have
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
What You'll Do.
Develop AI-powered features
Support prompt engineering
Evaluate models and workflows
Support debugging AI workflows
Participate in code reviews
Learn production engineering practices
How You'll Work.
Team & Collaboration
Work closely with senior engineers; Participate in design discussions; Participate in team planning
Communication Scope
Clear written communication skills
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