What you’ll do - Design, develop, and deploy ML, DL, and Generative AI models that solve complex business problems and deliver measurable value. - Build and maintain scalable ML pipelines and agentic workflows using modern frameworks and cloud-native tools. - Collaborate with data scientists, product managers, and engineers to translate requirements into AI-powered solutions. - Implement and optimize AI solutions using platforms such as GCP Vertex AI, AWS Bedrock/SageMaker, and Snowflake Cortex. - Apply prompt engineering, retrieval-augmented generation, fine-tuning, and RLHF to enhance model performance. - Develop and deploy autonomous AI agents using LangChain, LangGraph, and AgentSpace. - Integrate vector databases (e.g., PGVector) and LLM orchestration tools to support retrieval and memory in generative systems. - Ensure robust MLOps practices including CI/CD, monitoring, versioning, and lifecycle management of models. - Stay current with AI research and industry trends, evaluating emerging tools and techniques for enterprise adoption. - Contribute to internal knowledge sharing, documentation, and best practices for responsible and ethical AI development.

What you’ll bring - Bachelor’s or Master’s degree in Computer Science, Engineering, or related

What you’ll do - Design, develop, and deploy ML, DL, and Generative AI models that solve complex business problems and deliver measurable value. - Build and maintain scalable ML pipelines and agentic workflows using modern frameworks and cloud-native tools. - Collaborate with data scientists, product managers, and engineers to translate requirements into AI-powered solutions. - Implement and optimize AI solutions using platforms such as GCP Vertex AI, AWS Bedrock/SageMaker, and Snowflake Cortex. - Apply prompt engineering, retrieval-augmented generation, fine-tuning, and RLHF to enhance model performance. - Develop and deploy autonomous AI agents using LangChain, LangGraph, and AgentSpace. - Integrate vector databases (e.g., PGVector) and LLM orchestration tools to support retrieval and memory in generative systems. - Ensure robust MLOps practices including CI/CD, monitoring, versioning, and lifecycle management of models. - Stay current with AI research and industry trends, evaluating emerging tools and techniques for enterprise adoption. - Contribute to internal knowledge sharing, documentation, and best practices for responsible and ethical AI development.

What you’ll bring - Bachelor’s or Master’s degree in Computer Science, Engineering, or related