5.0 - 10.0 years

0 - 2 Lacs

Hyderabad

Hybrid

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.

Posted 1 week ago

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Gen AI Engineer Virtusa

5.0 - 10.0 years

5 - 10 Lacs

Pune, Maharashtra, India

On-site

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Proven experience in developing and implementing Generative AI models and algorithms, with a strong understanding of deep learning fundamentals. Experience with training and fine-tuning Generative models on high-performance computing infrastructure. Experience in working with Vector DB and Embedding Proficiency in programming languages such as Python, NLP, TensorFlow, PyTorch, or similar frameworks for building and deploying AI models. Strong analytical and problem-solving and collaboration skills A passion for exploring new ideas, pushing the boundaries of AI technology, and making a meaningful impact through your work.

Posted 2 weeks ago

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AI System Engineer Aurus

2.0 - 4.0 years

3 - 7 Lacs

Pune

Work from Office

Essential Skills: Machine Learning & Deep Learning: Solid understanding of ML concepts and hands-on experience with deep learning (especially neural networks and Transformers). Python Programming: Strong Python coding skills with familiarity in frameworks like TensorFlow, PyTorch, or Keras. Natural Language Processing (NLP): Experience with tokenization, embeddings, and language model fine-tuning. Data Engineering: Ability to clean, process, and manage large datasets with a focus on data quality. Math & Statistics: Good knowledge of linear algebra, calculus, probability, and statistics. Preferred Additional Skills: Model Deployment (MLOps): Exposure to deploying models using Docker, APIs, CI/CD, and tools like MLflow or Hugging Face Hub. RAG & LLM Integration: Hands-on with FAISS, LangChain, embedding models, and large language models. Prompt Engineering: Skilled in designing prompts and evaluating AI output quality. System Scalability: Understanding of GPU optimization and AI system performance.

Posted 3 weeks ago

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