
Data Scientist – Agentic AI
17 hours ago
Job Title: Data Scientist – Agentic AI & MLOps
Location: Bangalore - Hybrid (3 days work from office, 2 days from home)
About Us:
Our client delivers next-generation security analytics and operations management. They secure organisations worldwide by staying ahead of cyber threats, leveraging AI-reinforced capabilities for unparalleled protection.
Job Overview:
We're seeking a Senior Data Scientist to architect agentic AI solutions and own the full ML lifecycle—from proof-of-concept to production. You'll operationalise LLMs, build agentic workflows, implement MLOps best practices, and design multi-agent systems for cybersecurity tasks.
Key Responsibilities:
- Operationalise 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 containerised with Docker and Kubernetes.
- Monitor models and infrastructure through CloudWatch, Prometheus, and Grafana; analyse performance and optimise for cost and SLA compliance.
- Collaborate with data scientists, application developers, and security analysts to integrate agentic AI into existing security workflows.
Qualifications:
- Bachelor's or Master's in Computer Science, Data Science, AI or related quantitative discipline.
- 4+ 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.
- Extensive AWS experience with Bedrock, SageMaker, Lambda, EKS/ECS, S3 (Athena, Glue, Snowflake preferred).
- Infrastructure as Code skills with Terraform.
- Experience building REST APIs, microservices, and containerization with Docker and Kubernetes.
- Solid data science fundamentals: feature engineering, model evaluation, data ingestion.
- Understanding of cybersecurity principles, SIEM data, and incident response.
- Excellent communication skills for both technical and non-technical audiences.
Preferred Qualifications:
- AWS certifications (Solutions Architect, Developer Associate).
- Experience with Model Context Protocol (MCP) and RAG integrations.
- Familiarity with workflow orchestration tools (Apache Airflow).
- Experience with time series analysis, anomaly detection, and machine learning.
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