AI and Machine Learning Engineer

• Design moderate to complex AI/ML based cloud application features as per specifications. 

• Develop, train, evaluate, and deploy ML/DL models. 

• Build GenAI applications using LLMs, RAG, and Agent workflows. 

• Develop data pipelines and AI services/APIs. 

• Implement MLOps and LLMOps practices for deployment and monitoring. 

• Evaluate model and application performance using AI observability tools. 

• Collaborate with business and engineering teams to deliver AI solutions. 

• Develops and maintains GenAI / cloud application modules adhering to security policies. 

• Designs test plans, develops, executes, and automates test cases for assigned portions of the developed code. 

• Deploys code and troubleshoots issues in application modules and the deployment environment. 

• Shares and reviews innovative technical ideas with peers, high-level technical contributors, technical writers, and managers. 

Knowledge and Skills 

• Python, SQL, Data Analysis 

• Machine Learning and Deep Learning (Scikit-learn, PyTorch/TensorFlow) 

• LLMs, Prompt Engineering, RAG 

• LangChain, LangGraph, LlamaIndex (or similar frameworks) 

• Vector Databases and Semantic Search (Weaviate, Pinecone, Qdrant, pgvector, etc.) 

• AI Evaluation & Monitoring (MLflow, Langfuse, OpenTelemetry (basic understanding), Arize Phoenix) 

• Docker, Kubernetes, CI/CD. Understanding DevOps practices like continuous integration/deployment and orchestration with Kubernetes. 

• AWS, Azure, or GCP 

• Experience with design methodologies, cloud-native applications, developer tools, managed services, and next-generation databases. 

• Good written and verbal communication skills. 

What you need to bring: 

• Bachelor's degree in computer science, engineering, information systems, or closely related quantitative discipline. Master’s desirable. 

• Typically, 6+ years of overall experience and hands-on experience in mentioned technologies