Gen AI Developer-Full time-Hybrid
Why This Role Stands Out
This hybrid role offers the opportunity to build cutting-edge Generative AI and LLM applications, leveraging your Python, RAG, and cloud expertise to drive innovation. If you're a mid-senior developer passionate about AI and seeking to shape enterprise solutions, this exciting position at a reputable tech company is perfect for you to apply.
Quick Overview
Job Description
Python | Generative AI | LLM | RAG | Prompt Engineering | REST APIs | LangChain | Vector Databases | AWS/Azure/OpenAI
Job Summary
We are looking for an experienced GenAI Developer to design, develop, and deploy enterprise-grade Generative AI and LLM-powered applications. The ideal candidate should have strong Python development experience along with hands-on expertise in LLM integration, RAG, prompt engineering, AI agents, APIs, vector databases, and cloud-based AI services. Current GenAI roles commonly combine Python with RAG, agents, vector search, LLM APIs, and production deployment.
Key Responsibilities
- Design and develop Generative AI / LLM-powered applications for enterprise use cases.
- Integrate foundation models from OpenAI, Azure OpenAI, AWS Bedrock, Anthropic, or Google Gemini.
- Develop RAG (Retrieval-Augmented Generation) pipelines for enterprise knowledge applications.
- Implement document ingestion, chunking, embeddings, retrieval, reranking, and response generation.
- Design and optimize prompt engineering strategies for different business use cases.
- Build AI-powered applications such as:
- Enterprise chatbots
- AI assistants / copilots
- Document summarization
- Question-answering systems
- Knowledge assistants
- Document intelligence
- Develop AI Agents / Agentic AI workflows capable of tool calling and multi-step task execution.
- Integrate LLMs with enterprise applications, databases, REST APIs, and external tools.
- Build backend services and APIs using Python, FastAPI, or Flask.
- Develop reusable components for AI workflows and LLM integrations.
- Implement guardrails, validation, security, and responsible AI practices.
- Evaluate LLM responses for accuracy, hallucinations, consistency, and relevance.
- Optimize AI applications for latency, scalability, reliability, and token/cost efficiency.
- Deploy and support GenAI applications in cloud environments.
- Monitor production AI applications and troubleshoot issues.
- Collaborate with Data Scientists, ML Engineers, Software Engineers, Product Owners, and Architects.
Required Technical Skills
- Strong hands-on experience with Python.
- Strong understanding of Generative AI and Large Language Models (LLMs).
- Experience with Prompt Engineering.
- Hands-on experience building RAG applications.
- Experience with embeddings and semantic search.
- Experience with vector databases such as:
Skills
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