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.