AI Engineer

About the job The world’s most critical--and at risk--business applications have been neglected for far too long. Onapsis eliminates this blind spot by providing cybersecurity solutions dedicated to business-critical applications. Whether running on premises, in the cloud, or in a hybrid environment, Onapsis helps nearly 30% of the Forbes Global 100 understand the threats and risks across their SAP and Oracle landscapes. We are seeking an AI Engineer to join our mission-driven team. In this role, you will drive the incorporation of Gen AI, ML, directly into our product design. You will be at the forefront of shifting our product strategy and we want you to champion next-generation intelligent systems that fundamentally elevate our cybersecurity solutions. What you will be doing, your legacy: You will be working directly with company Principal Engineers evaluating, scoping, proposing, and building features to fulfill business solution requirements to protect our customers. You will play a direct role in laying the technical foundation for a new product offering. Additionally, you will be working with Data Engineering and DevOps to deliver high-quality products and services while also working closely with security and IT professionals to ensure safe and secure best practices are followed. Responsibilities: Bridge Data & AI Systems : Partner with core data engineers to allow ingestion and consumption via Model Context Protocol (MCP) Adapt & Tune Local Models Adapt open-weights Small Language Models (SLMs) using LoRA and QLoRA fine-tuning. Optimize and deploy these models to run securely on proprietary infrastructure containing sensitive data. Architect & Implement Advanced Retrieval Systems: Design and implement hybrid retrieval architectures, including Retrieval-Augmented Generation (RAG), Cache-Augmented Generation (CAG), and Knowledge-Augmented Generation (KAG). Leverage vector databases and structured knowledge graphs to deliver rich, grounded context to downstream models. Implement production-grade LLM /NLI observability and evaluation tools to monitor latencies, token consumption, and agent behavior in real time. Evaluate model outputs for factual grounding, accuracy, and compliance Host, serve and scale fine-tuned open-weights models utilizing low-latency runtimes like vLLM Translate complex agentic workflows and LLM concepts into clean, maintainable software architectures. Qualifications: 5+ years of software, data, or systems engineering experience. 2+ years of hands-on experience building and deploying production-grade NLP, LLM, or multi-agent applications. Strong proficiency in Python and REST API development. Deep understanding of API architecture patterns and multi-tenant system design. Experience working with distributed data stacks and platforms such as Apache Spark, Databricks or Snowflake. Experience with a major cloud provider: AWS (preferred) / Azure / GCP. Understanding of deep learning concepts and Transformer architectures. Hands-on experience with model quantization formats Experience with AI evaluation frameworks and LLM observability tools. Experience with AWS Sagemaker / AWS Bedrock other AWS AI services is a plus. Practical experience with LLM orchestration frameworks and multi-agent system design. Familiarity with Model Context Protocol (MCP) specifications and vector database implementations. What we offer: A role in shaping the future of protecting the most critical applications that run the world's business and a career that grows as the company grows. A unique culture of high achievement and teamwork. Supportive and humble colleagues are the space's top problem solvers and innovators. Financial security through competitive compensation and incentives (meal vouchers, private medical subscription, a generous leave package that includes 25 vacation days, 4 volunteering days through our OnaCares Program, work anniversary day off, a dedicated peer recognition platform, Focus Fridays, LinkedIn Learning).