Ml Engineer

Job Description

Role: ML Engineer. Exp : 5 Years to 10 Years Location : Hyderabad. Job Overview: Were seeking a ML Engineer / Data Scientist to architect agentic AI solutions and own the full ML lifecycle—from 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) 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 costs 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). Nice to have Experience with Model Context Protocol (MCP) and RAG integrations. Nice to have Experience in Crew.AI Familiarity with workflow orchestration tools (Apache Airflow). Experience with time series analysis, anomaly detection, and machine learning.