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Role Overview: Architect and lead the development of the application layer for enterprise GenAI solutions. Connect LLM backends to scalable frontends while managing API gateways and cloud deployments.
Key Responsibilities • • •
Application Architecture: Design scalable microservices that handle LLM requests, streaming responses (Server-Sent Events), and context management.
Cloud & DevOps: Oversee the deployment of AI applications on AWS, Azure, or GCP. Integrate CI/CD pipelines for AI software components.
Frontend & Backend Integration: Ensure seamless, low-latency integration between modern frontends (React/Next.js) and Python/FastAPI backends running AI models.
Required Skills & Qualifications • •
Tech Stack: Python (FastAPI/Django), JavaScript/TypeScript (React, Node.js), Docker, Kubernetes, AWS/Azure AI services.
Qualifications: Bachelor's/Master's in CS; 7 years in full-stack development with a strong recent focus on integrating AI/ML models into web apps.
Job ID: 150691459
Skills:
Tensorflow, Nlp, Pytorch, Docker, Python, AWS, Gcp, Azure, Kubernetes, GloVe, Hugging Face, vector databases, MLflow, Pinecone, LLM architecture, Word2Vec, RAG pipelines, ChromaDB, fine-tuning techniques, APIs from major AI providers, LangChain, LLM training methodologies, LoRA, QLoRA, sentence transformers
Skills:
Apis, Microservices, Azure ML, MLops, Docker, Rest Apis, Python, LangChain, Prompt engineering, Agentic AI design patterns, Data leakage protection, LLMOps, Vector databases, LLM security, Azure OpenAI, Semantic Kernel, LangGraph, Azure AI Studio, Neo4j GraphDB
Skills:
Node.js, Jinja2, Sql, Typescript, FastAPI, Python, LangChain, Hugging Face, Pinecone, Transformers, Milvus, LlamaIndex
Skills:
Deep Learning, Machine Learning, AWS, Cloud Services, Python, Azure, Gcp, AutoGen, NLP Libraries, Lang Chain, Distributed Computing Frameworks, Lang Graph, OpenAI Assistants API
Skills:
Python, Azure, AWS, Docker, Kubernetes, Llm
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