AI Systems Architect Lead

1 week ago

jaipur, India beBeeai Full time

Lead AI Systems ArchitectWe're looking for a highly skilled Lead AI Systems Architect to design and deliver intelligent, scalable AI solutions. As part of our team, you will focus on building production-grade systems involving LLMs, vector databases, agent-based workflows, and Retrieval-Augmented Generation (RAG) architectures on the cloud.About the RoleYou will contribute to the development of innovative AI systems that meet our high standards for scalability, reliability, and performance.You will work closely with cross-functional teams to ensure seamless integration of AI solutions with existing systems and applications.You will collaborate with data scientists and engineers to develop and deploy AI models that drive business value.You will stay up-to-date with industry trends and emerging technologies to inform your design decisions.Must-Have SkillsCoding Ability: Strong coding ability in Python and proficiency in SQL.Vector Databases: Hands-on experience with vector databases (e.g., Pinecone, FAISS, Weaviate).LLMs: Practical experience with LLMs (OpenAI, Claude, Gemini, etc.) in real-world workflows.Orchestration Tools: Familiarity with LangChain, LlamaIndex, or similar orchestration tools.Cloud-Native Architecture: Proven track record in delivering scalable AI systems on public cloud (Azure, AWS, or GCP).RAG Pipelines: Experience building and optimizing RAG pipelines.Serverless/Cloud-Native Architecture: Solid understanding of server less/cloud-native architecture and event-driven design.FastAPI/Flask/Django: Integrate/expose the ai solution in applications using fastapi,flask,djangoGood to Have SkillsAgentic AI Patterns: Exposure to agentic AI patterns and multi-agent coordination.AI System Safety Practices: Knowledge of AI system safety practices (e.g., hallucination filtering, grounding).MLOps Tools: Experience with MLOps tools (MLflow, KubeFlow) and CI/CD for ML.Concept/Data Drift Strategies: Understanding of concept/data drift and retraining strategies in production.Multi-Modal AI Pipelines: Experience working on AI projects involving classification, regression, and clustering models.Prior Work on Multi-Modal AI: Prior work on multi-modal AI pipelines (vision + language).Inference Tuning: Familiarity with real-time inference tuning (batching, concurrency).Ai Solutions Delivery: Demonstrated ability to deliver a variety of AI solutions in production environments across domains.

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