4.0 - 8.0 years

0 Lacs

karnataka

On-site

As a Senior Data Scientist at our client's organization, your role will involve architecting agentic AI solutions and overseeing the entire ML lifecycle, from proof-of-concept to production. You will play a key role in operationalizing large language models, designing multi-agent AI systems for cybersecurity tasks, and implementing MLOps best practices. 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 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: - Bachelors or Masters 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.,

Posted 4 days ago

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