Senior AI Engineer (RAG | Pinecone | GenAI Workflows)

About the RoleUplevyl is seeking aSenior AI Engineerto lead the design and deployment ofAI-powered, agentic workflowsthat shape the future of personalized insights. You will focus onvector search, retrieval-augmented generation (RAG), and intelligent automation , working closely with full-stack engineers and product teams to bring scalable GenAI features into production. This role is ideal for someone passionate aboutapplied AI and engineeringwith the ability to build, optimize, and deploy AI systems that go beyond prototypes and intoreal-world, production-grade systems .Key Responsibilities AI & Agentic WorkflowsDesign and implementRAG pipelinesfor semantic search, personalization, and contextual enrichment. Buildagentic AI workflowsusing Pinecone, LangChain/LangGraph, and custom orchestration. Integrate LLM-driven features into production systems, balancing innovation with scalability. Vector Search & Data IntelligenceArchitect and optimizevector databases (Pinecone, FAISS, Milvus)for low-latency retrieval. Work with structured/unstructured datasets forembedding, indexing, and enrichment . Collaborate with data engineers onETL/ELT pipelinesto prepare data for AI applications. Collaboration & Agile DeliveryPartner with backend and frontend engineers to integrate AI features into user-facing products. Participate in Agile ceremonies (sprint planning, reviews, standups). Maintain clear documentation and support knowledge sharing across the AI team.Tech Stack AI Tools:Pinecone, LangChain, LangGraph, OpenAI APIs (ChatGPT, GPT-4/5), HuggingFace models Languages:Python (primary for AI workflows), basic Node.js knowledge for integration Cloud & DevOps:AWS (Lambda, S3, RDS, DynamoDB, IAM), Docker, CI/CD pipelines Data Engineering:SQL, Python (Pandas, NumPy), ETL/ELT workflows, Databases (Postgres, DynamoDB, Redis) Bonus Exposure:React, Next.jsRequired Qualifications5+years inAI/ML engineering or software engineering with applied AI focus . Hands-on experience withRAG pipelines, vector databases (Pinecone, FAISS, Milvus), and LLM integration . Strong background inPython for AI workflows(embeddings, orchestration, optimization). Familiarity withagentic architectures(LangChain, LangGraph, or similar). Experience deploying and scaling AI features onAWS cloud environments . Strong collaboration and communication skills for cross-functional teamwork.Preferred SkillsExperience withembedding models , HuggingFace Transformers, or fine-tuning LLMs. Knowledge ofcompliance frameworks(GDPR, HIPAA, SOC 2). Exposure to personalization engines, recommender systems, or conversational AI.

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