ML Engineer

You will be responsible for setting up and maintaining the RAG backend infrastructure, ensuring data security and compliance, and integrating multiple data sources into a unified knowledge system. Over time, you will transition into broader engineering tasks, supporting the work of the engineering functions.

Responsibilities:

• Design, implement, and maintain scalable data ingestion pipelines for unstructured and structured content.
• Build and manage vector database infrastructure (e. g., Pinecone, Milvus) for document indexing and retrieval.
• Develop and enforce fine-grained access control mechanisms to ensure secure and compliant knowledge access.
• Collaborate with domain experts to source, curate, and structure knowledge for RAG workflows.
• Integrate document chunking, embedding generation, and metadata tagging into the pipeline.
• Monitor and optimize retrieval performance, latency, and quality across the RAG stack.
• Implement observability, logging, and evaluation metrics for pipeline health and retrieval quality.
• Work with IT teams to deploy and maintain services in a self-hosted environment.

Requirements:

• Bachelor's or master's degree in computer science, data engineering, or a related field.
• 5-8 years of experience in ML engineering, data engineering, or MLOps.
• Proficiency in Python and experience with data manipulation, analysis, and visualization libraries (e. g., Pandas, NumPy, Matplotlib, Seaborn, etc. )
• Hands-on experience with vector databases and embedding techniques and data pipelines.
• Familiarity with access control models (e. g., RBAC, ABAC) and data governance practices.
• Strong understanding and adherence to the Clean Code principles.

Preferred Qualifications:

• Experience with Retrieval-Augmented Generation (RAG) architectures.
• Hands-on experience with AI-aided software development (e. g., GitHub Copilot, Cursor, Claude Code).
• Knowledge of SQL and relational database designs. (e. g., PostgreSQL).
• Familiarity with LangChain, LlamaIndex, or similar frameworks.
• Exposure to knowledge management systems or enterprise search platforms.
• Understanding the basics of ISO 27001
• Experience with containerization (Docker, Kubernetes).
• Experience with ML/AI frameworks (e. g., SciKit-learn, Keras, PyTorch, TensorFlow).

Soft skills:

• Continuous learning mindset and a passion for staying up to date with the latest trends and advancements in data science and machine learning.
• Strong analytical and problem-solving skills.
• Ability to work within a team with a proactive attitude.
• Fluent in English.