Machine Learning Engineer

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.
• Strong 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.