Senior AI Engineer (RAG | Pinecone | GenAI Workflows)

About the Role   Uplevyl is seeking a Senior AI Engineer to lead the design and deployment of AI-powered, agentic workflows that shape the future of personalized insights. You will focus on vector 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 about applied AI and engineering with the ability to build, optimize, and deploy AI systems that go beyond prototypes and into real-world, production-grade systems .  Key Responsibilities   AI & Agentic Workflows   Design and implement RAG pipelines for semantic search, personalization, and contextual enrichment.  Build agentic AI workflows using Pinecone, LangChain/LangGraph, and custom orchestration.  Integrate LLM-driven features into production systems, balancing innovation with scalability.  Vector Search & Data Intelligence   Architect and optimize vector databases (Pinecone, FAISS, Milvus) for low-latency retrieval.  Work with structured/unstructured datasets for embedding, indexing, and enrichment .  Collaborate with data engineers on ETL/ELT pipelines to prepare data for AI applications.  Collaboration & Agile Delivery   Partner 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.js  Required Qualifications   5+ years in AI/ML engineering or software engineering with applied AI focus .  Hands-on experience with RAG pipelines, vector databases (Pinecone, FAISS, Milvus), and LLM integration .  Strong background in Python for AI workflows (embeddings, orchestration, optimization).  Familiarity with agentic architectures (LangChain, LangGraph, or similar).  Experience deploying and scaling AI features on AWS cloud environments .  Strong collaboration and communication skills for cross-functional teamwork.  Preferred Skills   Experience with embedding models , HuggingFace Transformers, or fine-tuning LLMs.  Knowledge of compliance frameworks (GDPR, HIPAA, SOC 2).  Exposure to personalization engines, recommender systems, or conversational AI. 

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