AI Engineer

• Join a project developing advanced information systems for the automotive sector.
• Support and automate service management, logistics, and e-commerce workflows.
• Work in an AI-native engineering environment where coding agents are used.
• Take ownership of problems end-to-end (DB → API → UI → ship).

Junior Full-stack Engineer, AI-Native Development

• Design and build AI-enabled features for various education products, from assessments to tutoring.
• Work with education partners and internal stakeholders to understand assessment practices, quality expectations and real-world constraints.
• Build evaluation approaches that measure output quality, consistency, usefulness and failure modes.
• Develop retrieval, prompt-management, orchestration and structured-output workflows where appropriate.
• Integrate LLM APIs, AI services and education data into reliable product capabilities.
• Design practical human-review, control and escalation workflows for AI-supported decisions.
• Improve the reliability, latency, cost and quality of AI services in production.
• Build prototypes quickly, test them against real education use cases and productionise successful approaches.
• Develop suitable guardrails, monitoring and auditability for AI outputs.
• Work with Backend Engineers on service architecture, data handling, integrations and scalability.
• Work with Product Engineers to ensure AI features are understandable, useful and trustworthy for teachers and students.

• Build AI applications using Python, LLMs & RAG
• Conduct research with new LLM technologies and frameworks (with a focus on AI coding assistants) to keep our AI capabilities at the cutting edge and share your findings with others
• Collaborate with cross-functional teams to identify AI-driven opportunities and integrate various solutions into their operations
• Fine-tune and optimize prompt strategies to enhance accuracy, reliability, and efficiency of LLM outputs
• Define AI evaluation metrics and benchmarking frameworks and implement monitoring systems to track LLM performance and costs
• Promote the adoption and understanding of AI within Oxylabs.

Data Engineer, AI

• Design, develop, and deploy AI-powered solutions that support industrial automation, smart manufacturing, and digital transformation initiatives.
• Develop and deploy GenAI applications (chatbots, copilots, RAG systems) using Azure OpenAI and LLMs.
• Build and maintain AI services and APIs on Microsoft Azure.
• Design scalable data pipelines for training and inference workflows.
• Implement and deploy ML models for predictive analytics, anomaly detection, and automation use cases.
• Containerize and deploy AI solutions using Docker and cloud services (AKS, Azure Functions).
• Build and manage vector databases and retrieval systems for enterprise/industrial data.
• Ensure monitoring, versioning, and lifecycle management of AI models (MLOps).
• Collaborate with cross-functional teams to integrate AI into industrial and enterprise systems.
• Stay updated with emerging AI/ML trends, cloud technologies, and DevSecOps advancements.

• Architect and build the agentic systems behind the Investigation Agent, including LangGraph workflows, tool integration, memory, reasoning, and orchestration.
• Own context engineering: determine what information the agent sees, when it sees it, and how it's structured to maximize performance and reliability.
• Build feedback loops, evaluation frameworks, and tracing infrastructure (Langfuse and beyond) to make agent development measurable and iterative.
• Drive key architectural decisions around agent state, long-running workflows, failure recovery, deployment, rollbacks, latency, and cost at scale.
• Own complex backend engineering work end-to-end across our stack: Python, FastAPI, Postgres, Redis, Kubernetes, ensuring agent systems are reliable, observable, and production-ready.
• Partner with Product, UX, Data Science, and Engineering to shape solutions, provide technical guidance, and align features with the realities of current AI capabilities.
• Champion AI-native development practices, leveraging tools like Claude Code, Cursor, and similar coding agents while promoting automation and team-wide adoption.
• Help define Hawk’s core engineering standards for agentic products, including testing, evaluation, deployment, monitoring, and trustworthiness.

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