Our Client Currently looking for Senior Lead/Architect – Generative AI

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 :

– 7+ 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 .

– 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)

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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