About Us
Data Oriented Defence Operations (DODO OÜ) is a cutting-edge Cybersecurity boutique based in Tallinn, Estonia. We operate at the forefront of the Deep Tech industry, and yes, it's time to say it openly: we proudly emerged from the NATO ecosystem. We specialize in advanced Defence and Security solutions, pushing the boundaries of what is possible in autonomous offensive security.
The Role
We are looking for a Python AI Engineer (Junior) to join our development team and work on ARES, our autonomous red-teaming platform powered by Agentic AI.
This is a part-time position of approximately 20 hours per week, fully remote, with flexible scheduling — it's designed to be compatible with a Master's program or other ongoing commitments.
Given the stage of our product development, we're looking for someone who has already spent real time building agent systems — ideally with LangChain and LangGraph — and who is ready to contribute to a live, complex agentic infrastructure early on. You'll be closely mentored by senior profiles in AI and Cyber Operations, so the learning curve is steep and well-supported; we simply want that curve to start from solid hands-on ground rather than from scratch.
Eligibility
Given the nature of our work in the Defence and Security sector, we are currently able to consider candidates who hold European (EU/EEA) citizenship and are resident in Europe. We appreciate the interest of candidates outside these criteria, but unfortunately we cannot move forward with those applications at this stage.
Compensation & Company Stage — Please Read Carefully
We want to be completely transparent about where DODO stands today, so you can make an informed decision.
We are pre-funding and currently working to close our first investment round. Concretely:
- The role begins with a 4-month trial period, which is unpaid. During this period we are not in a position to provide either salary or equity.
- If the funding round closes, the role converts to a paid position with stock options, on terms set out in the Memorandum of Understanding we sign at the outset.
- If the round does not close, we will not be able to offer either. We would rather say this openly now than have you discover it in four months.
We understand this is a significant ask, and it will not be the right fit for everyone — that's entirely reasonable. The ~20h/week structure is intended to make it workable alongside studies or other work. If it's not your situation right now, we genuinely understand, and we'd welcome hearing from you again once we're funded.
What You Will Do
- Agentic Backend Development: Design and optimize agent architectures using LangChain and LangGraph, supporting our CTO and the engineering team.
- Agentic RAG Pipelines: Build retrieval-augmented generation pipelines that let agents dynamically query knowledge bases, vulnerability databases, and prior engagement data to inform autonomous decision-making.
- Observability & Evaluation: Instrument agent pipelines with Langfuse for tracing, evaluation, and debugging of autonomous agent behavior.
- Structured Data & Validation: Use Pydantic to define robust schemas for agent inputs/outputs, tool calls, and inter-agent communication.
- Memory & State Systems: Build and maintain memory layers (vector stores, relational/database-backed memory) that let agents persist context across sessions and tasks, and feed retrieval pipelines.
- Target & Vulnerable Environment Creation: Build automated scenarios and vulnerable targets that our offensive AI will attack during QA and UAT phases.
- Cross-functional Bridging: Act as a technical bridge between the Agentic AI development team and the operational Cybersecurity team for test integration.
What We Are Looking For
- Hands-on experience building agentic AI systems — projects you have actually built and can talk us through: the architecture you chose, what worked, and what you'd approach differently today.
- Practical experience with LangChain and LangGraph: structuring graphs, managing state across nodes, handling tool calling, and debugging agents that loop or stall.
- Excellent proficiency in Python.
- Solid understanding of RAG architectures: embeddings, chunking strategies, vector similarity search, and retrieval-grounded generation — ideally with experience tuning a pipeline yourself.
- Pydantic (or similar) for data validation and schema design.
- Comfort with databases and memory/state persistence patterns for stateful applications — vector stores as well as relational or document stores.
- Master's degree, enrolled or completed, in Computer Science, Cybersecurity, AI, or a related field.
- Ability to work autonomously on goal-oriented tasks in a fully remote environment, managing your own ~20h/week schedule.
- A genuine interest in offensive security, cloud architectures, and applied Artificial Intelligence.
Nice to Have
- Backend development experience in Python — building and consuming APIs (FastAPI, Flask, or similar), async programming, and structuring services that hold up in production.
- Langfuse or equivalent LLM observability and evaluation tooling.
- Exposure to offensive security tooling, CTFs, or pentesting fundamentals.
- Experience with multi-agent orchestration patterns beyond LangGraph.
- Familiarity with containerization and cloud deployment (Docker, and any major cloud provider).
What We Offer
- 100% Full-Remote, Flexible Part-Time (within Europe): ~20h/week, organized around your own schedule.
- Expert Mentorship: Daily collaboration with top-tier industry experts in AI and Cyber Operations.
- Deep Tech Impact: Direct involvement in a Deep Tech project within the Defence and Security sector, with tangible impact on our technology roadmap.
- Career Growth: Concrete path toward consolidation of the position and evolution into key architectural roles as we scale.
How to Apply
Apply with your CV. If your profile includes agentic AI projects — personal, academic, or professional — do make sure they're visible on your CV or LinkedIn, since that's what we'll be looking at most closely.
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