AI Engineer

Summary: · The successful candidate is expected to build and operationalize AI, automation and data solutions that improve cybersecurity analytics, decision-making and operational response. · The person will work with cybersecurity specialists and technology teams to turn business needs into secure, reliable and scalable solutions. Key Responsibilities: · Develop AI-enabled applications, agents, workflows and automation for cybersecurity use cases. · Translate operational challenges into testable use cases, prototypes and production solutions. · Integrate AI models with enterprise systems, APIs, data pipelines and cloud services. · Establish testing, evaluation, documentation, monitoring and operational handover practices. · Apply appropriate security, access control, human oversight and failure-handling safeguards. Skills: · Strong software development experience, particularly in Python and modern application frameworks. · Hands-on experience inbuilding Large Language Model (LLM) applications, agents, retrieval solutions or multi-step orchestration workflows. · Experience with APIs, data integration, cloud development (preferably AWS). · Working knowledge of DevOps or CI/CD practices, including automated testing, version control, secrets management, containerization, deployment pipelines, and production diagnostics. · Strong analytical, problem-solving and stakeholder collaboration skills. · Possess positive learning and collaborative mindset. · Good written and verbal communication skills. · Agile, fast learner and able to adapt to changes. Good to have: Experience in one or more of the following areas is advantageous. Candidates are not expected to cover all three groups. 1. Cybersecurity • Security operations (SecOps), such as vulnerability management, threat intelligence, incident response or detection engineering. • Red and blue teaming, including testing A systems for misuse and failure modes. • Enterprise identity, network, proxy, governance or data access controls. 2. Data and platforms • Data engineering or data lake house platforms, such as Databricks. • Open Cybersecurity Schema Framework (OCSF), security data normalization or cross-source event correlation. • Full-stack product development and UI/UX. 3. AI evaluation and serving • LLM evaluation, benchmarking, model selection or finetuning. • Model serving, inference optimization, GPU infrastructure or local model deployment.