Senior Data Scientist
2 weeks ago
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.
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.
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.
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.
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.
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).
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.
AWS certifications (Solutions Architect, Developer Associate).
Experience with Model Context Protocol (MCP) and RAG integrations.
Experience with time series analysis, anomaly detection, and machine learning.
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