C
Generative AI Engineer
Job Description
Key Deliverables
- Design, build, and deploy AI models using LLMs and generative AI techniques
- Integrate AI models with existing systems and design APIs for seamless application integration
- Troubleshoot, optimize, and enhance generative AI models for performance
- Develop, maintain, and optimize conversational flows for C1 Elly
Role Responsibilities
- Research and evaluate emerging generative AI technologies
- Provide US business hours support for Elly Flow and Control Prompt adjustments
- Collaborate across teams to design, test, and deploy AI-driven innovations
- Create and maintain detailed documentation on AI models and conversational flows
More Info
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About Company
C1
Skills:
DVC), MLOps tools (MLflow, Numpy, Docker, XGBoost, Python, AWS, Sql, Git, Pandas, Gcp, FastAPI, Rest Apis, Prompt engineering, scikit-learn, Monitoring frameworks, Embeddings, LangChain, RAG workflows, Serverless platforms, LightGBM, Similarity search, LlamaIndex, AgenticAI
Skills:
.NET, Docker, Python, Workflows, React, Postgressql, FastAPI, VSCode, Azure Document Intelligence, Azure Search, CoT, Github Copilot, Agent Orchestration, Agent Memory, Autogen, Azure Open AI, Open Telemetry, Semantic Kernel, Langgraph, FAISS, RAG, Microsoft Agent Framework, MCP, Prompt Engineering, Retrieval Augmented Generation, sentence transformers, pgVector
Skills:
Java, Apis, Scalability, Typescript, Containers, Python, AWS, LangChain, A2A MCP, Reliability, agent orchestration frameworks, operational requirements, event-driven APIs, CI CD, LangGraph, Security, AWS AgentCore, observability, cloud-native development
Skills:
Machine Learning, Tensorflow, Django, Devops, Typescript, React, Rest Apis, MLops, Angular, Pytorch, Flask, XGBoost, Oop, Javascript, Microservices, AWS, FastAPI, Kubernetes, Python, Azure, Docker, SOLID Design Patterns, IaC, Ddd, Scikit-learn, NoSQL databases
Skills:
Sql, Microservices, Nosql, Django, MLops, Flask, FastAPI, Restful Apis, Azure, Kubernetes, Python, Model Evaluation, Core AI, Llm, AIML, Docket, Data Processing Pipelines, RAG, MCP, Event-driven Architectures, Observability