Senior Data Scientist - AgenticAI

We're seeking a Senior Data Scientist to architect Agentic AI solutions and own the full ML lifecyclefrom proof-of-concept to production. You'll operationalize LLMs, build Agentic workflows, implement MLOps best practices, and design multi-agent systems for cybersecurity tasks.

Key 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.

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.