Company Description
We're Nagarro.
We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at a scale — across all devices and digital mediums, and our people exist everywhere in the world (18500+ experts across 40 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in!
Job Description
Requirements
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Experience : 7.5+ years
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Strong experience in software engineering, AI/ML development, or automation engineering, including hands-on experience building AI/ML solutions.
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Strong programming expertise in Python with experience using AI/ML libraries such as Pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow.
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Hands-on experience developing AI-powered automation using Large Language Models (LLMs), Azure OpenAI, OpenAI APIs, and prompt engineering techniques.
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Experience designing and implementing Retrieval-Augmented Generation (RAG) solutions for enterprise AI applications.
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Strong knowledge of Microsoft Azure services including Azure Machine Learning, Azure Functions, Logic Apps, Azure Event Hub, and Microsoft Sentinel.
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Experience developing REST APIs and microservices using FastAPI or Flask.
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Hands-on experience integrating AI solutions with SIEM, SOAR, security monitoring, and ticketing platforms.
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Good understanding of cybersecurity fundamentals including SIEM concepts, security monitoring, attack patterns, threat detection, MITRE ATT&CK framework, and log analysis.
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Experience building AI-powered alert automation, incident response workflows, and threat intelligence solutions.
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Familiarity with cloud platforms including Microsoft Azure, AWS, and Google Cloud Platform.
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Working knowledge of Git, Docker, CI/CD pipelines, containerization, and modern software development practices.
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Experience with Azure Sentinel Analytics Rules, Playbooks, Workbooks, or similar security automation capabilities is preferred.
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Familiarity with SOAR platforms such as Microsoft Sentinel SOAR, LogRhythm SIEM, or equivalent security orchestration solutions.
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Knowledge of Google Cloud services including Security Command Center, Pub/Sub, and BigQuery is an advantage.
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Experience using LLM orchestration frameworks such as LangChain, Semantic Kernel, or equivalent AI frameworks is desirable.
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Familiarity with Azure AI Search (Cognitive Search), vector databases, and semantic search capabilities is preferred.
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Understanding of on-premises SIEM platforms and enterprise log aggregation tools is an added advantage.
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Strong analytical, troubleshooting, and problem-solving skills with the ability to build scalable AI-powered security automation solutions.
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Excellent communication and collaboration skills with experience working in Agile and cross-functional engineering teams.
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Bachelor's degree in Computer Science, Information Technology, Engineering, MCA, or a related discipline.
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Professional certifications such as Microsoft SC-200, AZ-900, CEH, CompTIA Security+, or equivalent cloud and cybersecurity certifications are desirable.
Responsibilities
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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.
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Build AI-powered security agents and bots that automate alert triage, investigation, and remediation processes.
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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.
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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.
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Build and optimize Retrieval-Augmented Generation (RAG) pipelines that leverage enterprise threat intelligence repositories, knowledge bases, and security playbooks.
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Develop, evaluate, and optimize LLM-powered security use cases through prompt engineering, model evaluation, and continuous performance improvement.
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Develop Azure Functions, Logic Apps, and Python-based automation to streamline alert enrichment, incident routing, notification workflows, and security operations.
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Build and maintain integrations with SIEM, SOAR, ticketing, monitoring, and security platforms using REST APIs, FastAPI, and custom connectors.
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Integrate AI-generated insights with incident management systems to automate ticket creation, prioritization, and status tracking.
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Develop Python-based APIs and microservices to expose AI capabilities for enterprise security applications.
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Consume, normalize, and process event streams from Azure Event Hub, GCP Pub/Sub, cloud platforms, and on-premises log sources.
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Develop unit tests, integration tests, and participate in peer code reviews to ensure secure, scalable, and high-quality software delivery.
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Monitor AI model performance, detect model drift, maintain dashboards, and continuously improve model accuracy using MLOps best practices.
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Maintain CI/CD pipelines for AI model deployment, automation releases, and infrastructure updates.
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Prepare technical documentation including API specifications, architecture diagrams, deployment guides, operational runbooks, and data models.
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Collaborate closely with SOC analysts, cybersecurity engineers, cloud teams, DevOps engineers, and data scientists to continuously improve AI-driven security automation.
Qualifications
Bachelor’s or master’s degree in computer science, Information Technology, or a related field.
