Roles and Responsibilities :

- Design and build scalable backend services and REST/gRPC APIs for AI-powered products.
- Architect and deploy cloud-native solutions on AWS using services such as ECS/EKS, Lambda, SQS, S3, RDS/Aurora, and Bedrock.
- Build and maintain LLM integration layers, including prompt pipelines, RAG systems, and vector stores.
- Develop and deploy agentic workflows using LangGraph, CrewAI, AutoGen, Semantic Kernel, or comparable frameworks.
- Own MLOps/LLMOps pipelines covering model serving, evaluation, monitoring, and observability.
- Collaborate with product, engineering, and machine learning teams to productionize AI capabilities.
- Apply appropriate security, reliability, scalability, and governance controls across the AI platform lifecycle.

Job Requirements :





- 3-6 years of experience in AI/ML Engineering or a related field.
- Solid backend engineering capability using Python and/or Node.js/Go.
- Hands-on AWS experience, including serverless architecture and container orchestration.
- Production experience with OpenAI, Anthropic, Amazon Bedrock, or open-source large language models.
- Hands-on experience with at least one agentic framework.
- Experience with vector databases such as Pinecone, Weaviate, or pgvector.
- Strong understanding of RAG architecture, embeddings, and prompt engineering.

📌 Machine Learning Engineer (Bengaluru)
🏢 kaara Info Systems
📍 Bengaluru