Gen AI Developer
Primary skills : Gemini Agent space, Experience in running AI services, vibe coding(Cursor), Vertex, Vector DB
Secondary skills : Multi cloud experience (AWS)
Key Responsibilities:
Design, implement, and optimize end-to-end RAG pipelines for critical business use cases, focusing on retrieval quality and scalability.
Develop robust, scalable GenAI solutions using Google Cloud's Vertex AI ecosystem (including Vertex AI Workbench, Feature Store, and MLOps tools).
Implement advanced RAG techniques, including strategic chunking, semantic boundary detection, negative sampling, and retrieval quality optimization.
Engineer and deploy multi-agent systems and autonomous AI solutions.
Ensure the production readiness of all AI systems by designing and implementing multi-layered security, PII redaction, input/output guardrails (toxicity, bias mitigation, factuality checks), and audit logging.
Establish A/B testing, human evaluation processes, and define standard RAG metrics (e.g., Precision@K, Recall@K) to measure and improve model performance.
Collaborate with engineering teams to ensure seamless deployment and operational excellence in a cloud-native environment.
Required Technical Skills & Experience:
5+ years of overall IT experience with a minimum of 2+ years of deep, hands-on experience in AI/ML engineering, specifically in Generative AI and LLMs.
Expertise in Google Cloud Platform services (GCP) for AI, including Vertex AI, BigQuery, Dataflow, and Cloud Run/Kubernetes. Hands on exposure to using GCP services for storage, serverless-logic, search, transcription, and chat.
Proven ability to design and operate RAG-at-scale.
Experience in Integration with MCP
Deep technical understanding of vector databases, dimensionality trade-offs, similarity metrics, and advanced reranking strategies.
Strong proficiency in Python, including modern AI/ML frameworks like LangChain, LangGraph, and/or CrewAI.
Must be proficient with AI-assisted development tools like Cursor and have demonstrable experience integrating and programming with large language models such as Anthropic's Claude.
Experience implementing MLOps best practices, CI/CD, and deployment automation.
Excellent problem-solving skills, particularly for debugging issues across the RAG lifecycle (chunking, embeddings, retrieval, LLM response
Primary skills : Gemini Agent space, Experience in running AI services, vibe coding(Cursor), Vertex, Vector DB
Secondary skills : Multi cloud experience (AWS)
Key Responsibilities:
Design, implement, and optimize end-to-end RAG pipelines for critical business use cases, focusing on retrieval quality and scalability.
Develop robust, scalable GenAI solutions using Google Cloud's Vertex AI ecosystem (including Vertex AI Workbench, Feature Store, and MLOps tools).
Implement advanced RAG techniques, including strategic chunking, semantic boundary detection, negative sampling, and retrieval quality optimization.
Engineer and deploy multi-agent systems and autonomous AI solutions.
Ensure the production readiness of all AI systems by designing and implementing multi-layered security, PII redaction, input/output guardrails (toxicity, bias mitigation, factuality checks), and audit logging.
Establish A/B testing, human evaluation processes, and define standard RAG metrics (e.g., Precision@K, Recall@K) to measure and improve model performance.
Collaborate with engineering teams to ensure seamless deployment and operational excellence in a cloud-native environment.
Required Technical Skills & Experience:
5+ years of overall IT experience with a minimum of 2+ years of deep, hands-on experience in AI/ML engineering, specifically in Generative AI and LLMs.
Expertise in Google Cloud Platform services (GCP) for AI, including Vertex AI, BigQuery, Dataflow, and Cloud Run/Kubernetes. Hands on exposure to using GCP services for storage, serverless-logic, search, transcription, and chat.
Proven ability to design and operate RAG-at-scale.
Experience in Integration with MCP
Deep technical understanding of vector databases, dimensionality trade-offs, similarity metrics, and advanced reranking strategies.
Strong proficiency in Python, including modern AI/ML frameworks like LangChain, LangGraph, and/or CrewAI.
Must be proficient with AI-assisted development tools like Cursor and have demonstrable experience integrating and programming with large language models such as Anthropic's Claude.
Experience implementing MLOps best practices, CI/CD, and deployment automation.
Excellent problem-solving skills, particularly for debugging issues across the RAG lifecycle (chunking, embeddings, retrieval, LLM response