Generative AI Engineer
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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:
Microservices, Rest Apis, MLops, Python, Docker, Apis, Azure ML, Prompt engineering, Neo4j GraphDB, Agentic AI design patterns, Vector databases, Semantic Kernel, LLMOps, Azure OpenAI, LLM security, LangChain, Data leakage protection, Azure AI Studio, LangGraph
Skills:
Cloud deployment, Tensorflow, Pandas, Api Integration, Pytorch, Python, Git, MLflow, Scikit-learn, Haystack, Azure OpenAI, Microsoft Copilot Studio, AI Foundry, LangChain, Transformers
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:
Java, Scala, Rest Apis, Python, Generative AI, Data Ingestion Pipelines, Go, Prompt Management, Vector Databases, Agentic AI, Multi-Agent Systems, AI Evaluation, AI ML technologies, Prompt Engineering
Skills:
Python, Azure, AWS, Docker, Kubernetes, Llm
