Role Summary:
Architect and operationalize Agentic AI solutions, managing the full ML lifecycle from proof-of-concept to production. Build multi-agent AI systems for cybersecurity tasks, implement MLOps best practices, and integrate AI workflows into security operations.
Key Responsibilities:
Operationalize large language models and Agentic workflows to automate threat response
Design, deploy, and maintain multi-agent AI systems for log analysis, anomaly detection, and incident response
Build GenAI proof-of-concepts and production-ready components on AWS using Bedrock, SageMaker, Lambda, EKS/ECS
Implement CI/CD pipelines for ML with GitHub Actions, Jenkins, and AWS CodePipeline
Manage model versioning, automated testing, rollback procedures, and retraining workflows
Automate cloud infrastructure with Terraform, build REST APIs, and containerize microservices with Docker and Kubernetes

Minimum Requirements:




Bachelor's or Master's in Computer Science, Data Science, AI, or related discipline
4+ years of software development, 3+ years in LLM-based/Agentic AI architectures
Solid knowledge of generative AI fundamentals, embeddings, vector databases, prompt engineering, and RAG
Hands-on experience with LangChain, LangGraph, LlamaIndex, Crew.AI, or equivalent frameworks
Python proficiency and production-grade coding for data pipelines and AI workflows
Deep MLOps knowledge including CI/CD for ML, model monitoring, automated retraining, and production-quality best practices
Extensive AWS experience, Terraform, Docker, Kubernetes, and REST API development
Understanding of cybersecurity principles, SIEM data, and incident response

📌 Senior Data Scientist Bengaluru (India)
🏢 Securonix Consultancy
📍 India