Data Scientist at Securonix designing AI solutions for cybersecurity. Operationalizing LLMs and AI workflows in a hybrid environment in Bangalore, Karnataka.
Responsibilities
Operationalize large language models and Agentic workflows (LangChain, LangGraph, LlamaIndex, Crew.AI) to automate security decision-making and threat response.
Design, deploy, and maintain multi-agent AI systems for log analysis, anomaly detection, and incident response.
Build proof-of-concept GenAI solutions and evolve them into production-ready components on AWS (Bedrock, SageMaker, Lambda, EKS/ECS) using reusable best practices.
Implement CI/CD pipelines for model training, validation, and deployment with GitHub Actions, Jenkins, and AWS CodePipeline.
Manage model versioning with MLflow and DVC, set up automated testing, rollback procedures, and retraining workflows.
Automate cloud infrastructure provisioning with Terraform and develop REST APIs and microservices containerized with Docker and Kubernetes.
Monitor models and infrastructure through CloudWatch, Prometheus, and Grafana; analyze performance and optimize for cost and SLA compliance.
Collaborate with data scientists, application developers, and security analysts to integrate agentic AI into existing security workflows.
Requirements
Bachelor’s or Master’s in Computer Science, Data Science, AI or related quantitative discipline.
6+ years of software development experience, including 3+ years building and deploying LLM-based/agentic AI architectures.
In-depth knowledge of generative AI fundamentals (LLMs, embeddings, vector databases, prompt engineering, RAG).
Hands-on experience with LangChain, LangGraph, LlamaIndex, Crew.AI or equivalent agentic frameworks.
Strong proficiency in Python and production-grade coding for data pipelines and AI workflows.
Deep MLOps knowledge: CI/CD for ML, model monitoring, automated retraining, and production-quality best practices.
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