Senior SOC Engineer (AI & Automation)

Job Description

Overview

You will leverage cloud-native technologies (Azure), DevOps practices, and MLOps tools to implement secure, compliant deployments, including vector search and RAG pipelines. Collaboration with cross-functional teams and mentoring junior engineers are integral to this position

Responsibilities

Responsibilities:

• Automation and AI technologies Management: Develop, implement, and maintain AI-driven full-stack solutions that enhance user experiences and optimize internal workflows.
• Backend Services & API Development: Develop and operate scalable backend services and APIs for AI/ML workloads using languages such as Python, Typescript, Node.js etc.
• AI/ML Platform Evolution: Build and enhance platform components such as, feature and embeddings stores, vector search/semantic search infrastructure, evaluation dashboards, prompt/version management, and feedback loops.
• Security & Responsible AI: Implement security, data protection, and responsible AI guardrails, ensuring safe and compliant use of models and data.
• Cloud-Native & DevOps Practices: Apply best practices using, Azure and on-prem containerization, CI/CD pipelines (Azure Devops / Gitlab). Infrastructure-as-Code for scalable deployments.
• Cross-Functional Collaboration: Work closely with Product, Data Science, Data Engineering, Design, and DevOps/SRE teams to translate business problems into robust technical solutions.
• Integration and Orchestration: Integrate Automation and AI technologies with existing SOC tools and technologies, orchestrating workflows across disparate security systems for seamless response coordination.
• Performance Monitoring and Optimization: Monitor the performance of the Automation and AI technologies, identifying and addressing any issues or bottlenecks to ensure optimal functionality and reliability.
• Documentation and Training: Maintain comprehensive documentation of AI workflows, configurations, and procedures. Provide training and guidance to internal users and engineers.

Qualifications

Skills:

• Proven hands-on experience deploying AI/ML and Generative AI solutions in production environments, including fine-tuning, and integrating transformer-based models, as well as designing and implementing RAG pipelines and advanced semantic search systems.
• Proficiency in programming experience in Python, Typescript, and scripting for automation.
• Experience with MLOps, including CI/CD for ML workflows, model deployment, and monitoring performance and data drift.
• Expertise in one or more AI/ML frameworks and tools such as Hugging Face Transformers, Agno AI, LangChain, MLflow, vLLM, Ollama, or equivalent platforms for model deployment and orchestration.
• Experience building and managing distributed, scalable services with robust observability (metrics, logs, traces, dashboards, alerts).
• Experience with MLOps, including CI/CD for ML workflows, model deployment, and monitoring performance and data drift.

• Any certification in AI and ML technologies.
• Any certification in platform and automation is a plus.

• Bachelor's or master's degree in computer science, Information Technology, Cybersecurity, or related field.
• A minimum of 7 years of experience in software engineer with strong exposure in machine learning, backend development and distributed systems.
• Prior experience in a technical role within a cybersecurity domain is preferred.