About ShadowDragon

ShadowDragon provides OSINT tools that help organizations identify individuals, investigate digital activity, and monitor evolving threats across online environments. Built for law enforcement, government, and enterprise security teams, we support the full intelligence workflow from identity resolution to investigation and ongoing monitoring. Our platform enables users to analyze publicly available data, uncover relationships, and generate actionable intelligence with speed and clarity. ShadowDragon helps teams move from fragmented data to structured intelligence so they can make informed decisions, respond faster, and operate with confidence in a rapidly changing digital landscape.

About the role

ShadowDragon is seeking a Junior AI/Machine Learning Engineer to support the development, testing, and continuous improvement of AI-powered capabilities across our data intelligence platform.

This role is designed for an early-career engineer with strong technical fundamentals, hands-on exposure to AI, and a genuine passion for building useful products with emerging technologies. You may have gained your experience through internships, academic work, workshops, bootcamps, open-source contributions, or independent projects. We value demonstrated curiosity and practical experience over a long list of credentials.

Working closely with senior engineers, you will contribute to prompt experimentation, retrieval-augmented generation (RAG) pipelines, data preparation, model evaluation, automated testing, documentation, debugging, and AI service development. You will gain practical experience with large language models, embeddings, retrieval systems, AI agents, machine learning workflows, cloud services, and production engineering practices.

Quality is an important part of this role. As we expand our AI capabilities, we need reliable ways to evaluate outputs, automate regression testing, and integrate new functionality safely into our existing platform. We are therefore especially interested in candidates who are curious about both building AI features and validating that they work as intended.

What you'll do

• Assist in developing and integrating AI-powered features using Python, LLM tools, machine learning libraries, APIs, and internal platform services.
• Support prompt engineering, prompt testing, model comparison, and the systematic evaluation of AI-generated outputs.
• Help design and maintain automated tests and regression-testing processes for AI features, services, and workflows.
• Contribute to RAG workflows, including document preparation, chunking, metadata tagging, embedding generation, retrieval testing, and result evaluation.
• Prepare, clean, format, sanitize, and validate structured and unstructured data used in AI experiments and model testing.
• Review model outputs, identify errors and recurring patterns, compare approaches, and clearly document findings.
• Write clean, readable, and maintainable Python code for scripts, prototypes, experiments, internal tools, and service components.
• Support the debugging of AI workflows, data pipelines, API integrations, and model behavior under the guidance of senior engineers.
• Work with existing codebases and services while following established development, testing, and review processes.
• Participate in code reviews, technical discussions, team planning, and knowledge-sharing activities.
• Learn and apply production engineering practices, including Git workflows, logging, testing, Docker, CI/CD, deployment fundamentals, and MLOps.
• Clearly document experiments, implementation decisions, evaluation results, and technical recommendations.

Required Qualifications

• Up to two years of experience in AI engineering, machine learning, software engineering, data science, or a related technical area.
• Relevant experience may come from internships, academic work, bootcamps, workshops, portfolio projects, hackathons, or open-source contributions.
• Demonstrated experience completing one or more hands-on, end-to-end AI or machine learning projects.
• Solid foundational Python programming skills.
• Foundational understanding of machine learning, deep learning, natural language processing, data processing, and model evaluation.
• Familiarity with one or more tools or libraries such as PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, pandas, or NumPy.
• Interest in LLMs, generative AI, prompt engineering, embeddings, semantic search, RAG, and AI agents.
• Ability to work with both structured and unstructured data.
• Comfort using Git, GitHub, command-line tools, notebooks, and APIs in a collaborative development environment.
• Strong attention to detail, curiosity, problem-solving ability, and willingness to learn from feedback.
• Clear written communication skills for documenting technical work and experimental results.
• A collaborative mindset and enthusiasm for building useful, reliable AI-powered products.

Preferred Qualifications

You do not need to meet every preferred qualification to apply. Experience in any of the following areas would be valuable:

• Project, academic, or internship experience involving LLMs, chatbots, semantic search, classification, summarization, automation, or other machine learning workflows.
• Knowledge of LLM evaluation metrics or established processes for testing LLM-powered applications.
• Experience with QA, automated testing, regression testing, or the validation of nondeterministic AI outputs.
• Exposure to vector databases, embeddings, document processing, information retrieval, or search systems.
• Familiarity with AWS, particularly Amazon Bedrock, cloud storage, or approaches to persisting chat and conversation data.
• Awareness of data sanitization, privacy, and security considerations in LLM applications, including bring-your-own-key architectures.
• Familiarity with Docker, CI/CD, MLOps, cloud environments, or basic deployment workflows.
• Experience using the Git command-line interface and GitHub-based collaboration workflows.
• Exposure to agentic engineering or AI-assisted software development.
• Familiarity with 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 Success Looks Like

Success in this role means consistently contributing to AI experiments and product development while steadily growing your production engineering skills. You will help the team increase its capacity by producing clean, reliable implementation work; improving model and workflow evaluation; supporting automated testing; documenting findings clearly; and assisting with debugging and integration.

Over time, you will take greater ownership of AI features and evaluation processes while developing into a well-rounded AI engineer who understands both how to build AI-powered systems and how to verify that they are reliable, maintainable, and ready for production.