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

Skills: Aws Cloud, Terraform, MLops, Python

Experience: 4.00-9.00 Years

📌 Senior Data Scientist - Agentic AI & MLOps (Bengaluru)
🏢 Securonix Consultancy
📍 Bengaluru