Senior ML Engineer

Job Description
 

We are a comprehensive SaaS platform that provides complete device operational intelligence, allowing security teams to effortlessly detect vulnerabilities, assess risks, and deploy cutting-edge mitigations. Our universal vulnerability repository is the first in the industry; it enables organisations, as well as IT and security solutions, to stay steps ahead of cyber threats, ensuring robust security in the face of evolving cyber threats.

🛠 Desired Skills and Experience

● Strong commitment to code quality, particularly in Python.

● In-depth knowledge of LLMs (e.g., GPT, Claude, Mistral) with hands-on experience in prompt engineering, fine-tuning, or RAG pipelines.

● Solid understanding of NLP fundamentals: tokenization, embeddings, and

transformer-based models.

● Proficiency with Python and key ML/NLP frameworks (e.g., HuggingFace,

Sentence-Transformers, LangChain, LangGraph).

Strong grasp of:
 

○ Scalable and maintainable software architecture

○ Object-oriented programming and design patterns

○ Automated testing (unit, integration, and smoke tests)

● Familiarity with Git and command-line tools.

● Excellent communication skills and professional-level English.

● Ability to learn quickly and work independently across time zones.

● Minimum of 5+ years of experience in machine learning, including at least 2 years in the generative AI space.

Nice to Have:
 

● Interest or background in cybersecurity.

● Experience with:

○ Task orchestration tools (e.g., Airflow, Argo Workflows)

○ Cloud platforms (preferably Google Cloud Platform)

○ SQL databases (e.g., PostgreSQL)

○ Graph databases (e.g., ArangoDB, Neo4J)

○ Infrastructure as code (Terraform)

○ Containerization and orchestration (Docker, Kubernetes)

🎯 Responsibilities
 

● Design and implement Retrieval-Augmented Generation (RAG) pipelines using state-of-the-art LLMs (e.g., OpenAI, Vertex AI, Cohere).

● Build pipelines for extracting structured information from unstructured sources (PDFs, HTML, etc.) using LLMs and advanced prompt engineering techniques.

● Conduct R&D in the cybersecurity domain using NLP techniques to match and analyze vulnerability data.

● Develop evaluation datasets and pipelines to benchmark the quality and correctness of LLM outputs.

● Craft effective prompts, fine-tune LLMs, and experiment with embedding and similarity methods.

● Design and implement HTTP APIs for both internal and customer-facing

applications.

● Research the capabilities and limitations of current LLMs and apply findings to enhance RAG and other LLM-based services.

● Write unit and integration tests to ensure reliability and maintainability.

● Participate in sprint planning and collaborate with non-technical stakeholders to gather and translate business requirements.

● Review code, designs, and technical deliverables, providing constructive feedback.