Senior Staff Engineer (AI Developer SOC Automation)

Requirements

• Experience : 7.5 years
• Strong experience in software engineering AI/ML development or automation engineering including hands-on experience building AI/ML solutions.
• Strong programming expertise in Python with experience using AI/ML libraries such as Pandas NumPy Scikit-learn PyTorch or TensorFlow.
• Hands-on experience developing AI-powered automation using Large Language Models (LLMs) Azure OpenAI OpenAI APIs and prompt engineering techniques.
• Experience designing and implementing Retrieval-Augmented Generation (RAG) solutions for enterprise AI applications.
• Strong knowledge of Microsoft Azure services including Azure Machine Learning Azure Functions Logic Apps Azure Event Hub and Microsoft Sentinel.
• Experience developing REST APIs and microservices using FastAPI or Flask.
• Hands-on experience integrating AI solutions with SIEM SOAR security monitoring and ticketing platforms.
• Good understanding of cybersecurity fundamentals including SIEM concepts security monitoring attack patterns threat detection MITRE ATT&CK framework and log analysis.
• Experience building AI-powered alert automation incident response workflows and threat intelligence solutions.
• Familiarity with cloud platforms including Microsoft Azure AWS and Google Cloud Platform.
• Working knowledge of Git Docker CI/CD pipelines containerization and modern software development practices.
• Experience with Azure Sentinel Analytics Rules Playbooks Workbooks or similar security automation capabilities is preferred.
• Familiarity with SOAR platforms such as Microsoft Sentinel SOAR LogRhythm SIEM or equivalent security orchestration solutions.
• Knowledge of Google Cloud services including Security Command Center Pub/Sub and BigQuery is an advantage.
• Experience using LLM orchestration frameworks such as LangChain Semantic Kernel or equivalent AI frameworks is desirable.
• Familiarity with Azure AI Search (Cognitive Search) vector databases and semantic search capabilities is preferred.
• Understanding of on-premises SIEM platforms and enterprise log aggregation tools is an added advantage.
• Strong analytical troubleshooting and problem-solving skills with the ability to build scalable AI-powered security automation solutions.
• Excellent communication and collaboration skills with experience working in Agile and cross-functional engineering teams.
• Bachelors degree in Computer Science Information Technology Engineering MCA or a related discipline.
• Professional certifications such as Microsoft SC-200 AZ-900 CEH CompTIA Security or equivalent cloud and cybersecurity certifications are desirable.

Responsibilities

• Design develop and maintain AI-powered automation solutions to enhance Security Operations Center (SOC) workflows including alert classification anomaly detection threat prioritization and incident response.
• Build AI-powered security agents and bots that automate alert triage investigation and remediation processes.
• Develop and fine-tune NLP and machine learning models for log parsing alert summarization phishing detection Indicator of Compromise (IOC) extraction and threat intelligence analysis.
• Design and implement feature engineering pipelines to process security telemetry from cloud and on-premises monitoring platforms including Microsoft Sentinel GCP Security Command Center Trend Micro XDR and SIEM solutions.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines that leverage enterprise threat intelligence repositories knowledge bases and security playbooks.
• Develop evaluate and optimize LLM-powered security use cases through prompt engineering model evaluation and continuous performance improvement.
• Develop Azure Functions Logic Apps and Python-based automation to streamline alert enrichment incident routing notification workflows and security operations.
• Build and maintain integrations with SIEM SOAR ticketing monitoring and security platforms using REST APIs FastAPI and custom connectors.
• Integrate AI-generated insights with incident management systems to automate ticket creation prioritization and status tracking.
• Develop Python-based APIs and microservices to expose AI capabilities for enterprise security applications.
• Consume normalize and process event streams from Azure Event Hub GCP Pub/Sub cloud platforms and on-premises log sources.
• Develop unit tests integration tests and participate in peer code reviews to ensure secure scalable and high-quality software delivery.
• Monitor AI model performance detect model drift maintain dashboards and continuously improve model accuracy using MLOps best practices.
• Maintain CI/CD pipelines for AI model deployment automation releases and infrastructure updates.
• Prepare technical documentation including API specifications architecture diagrams deployment guides operational runbooks and data models.
• Collaborate closely with SOC analysts cybersecurity engineers cloud teams DevOps engineers and data scientists to continuously improve AI-driven security automation.

Qualifications :

Bachelors or masters degree in computer science Information Technology or a related field.

Remote Work :

No

Employment Type :

Full-time