> Markdown version of [/jobs/ext/4569958-generative-ai-engineer](https://www.wearedevelopers.com/jobs/ext/4569958-generative-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Generative AI Engineer

- **Company:** Ova Technologies
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Software Applications, Microsoft Azure, Cloud Computing, Cloud Engineering, Computer Programming, Databases, Continuous Integration, Elasticsearch, Python (Programming Language), PostgreSQL, Machine Learning, MongoDB, Open Source Technology, Performance Tuning, OpenAI, Cloud Services, Tensorflow, Software Safety, Search Technologies, Pinecone, QLoRA, Google Cloud, Enterprise Software Applications, Chatbots, Pytorch, LangChain, Retrieval-Augmented Generation, Flask (Web Framework), Large Language Models, Multi-Agent Systems, Prompt Engineering, Hallucination Detection, Model Validation, Llamaindex, Generative AI, Backend, Agentic-AI, Git, Fastapi, Build Management, Kubernetes, Information Technology, Weaviate, Milvus, Machine Learning Operations, FAISS, Claude, Api Design, Google Gemini, Restful APIs, GPT, Automation Anywhere, Docker, ChromaDB
- **Published:** November 4, 2026
- **Apply:** https://www.thejobnetwork.com/job/455bf3b0-26fa-4c90-b111-f4e6a2dc58ed/generative-ai-engineer

## About the Role

* Strong programming experience in Python.
 * Hands-on experience with Generative AI and Large Language Models (LLMs).
 * Strong knowledge of:
 + Prompt Engineering
 + RAG
 + Embeddings
 + Vector Databases
 + Semantic Search
 + AI Agents
 + LLM APIs
 * Experience with frameworks such as LangChain, LangGraph, or LlamaIndex.
 * Experience working with vector databases such as Pinecone, FAISS, Weaviate, Milvus, or Chroma.
 * Knowledge of REST APIs and backend development.
 * Familiarity with Git, Docker, and CI/CD.
 * Understanding of machine learning and NLP concepts.

Preferred Skills

 * Experience with OpenAI, Azure OpenAI, Anthropic, Google Gemini, or AWS Bedrock.
 * Knowledge of fine-tuning, LoRA/QLoRA, and model evaluation.
 * Experience developing autonomous or multi-agent systems.
 * Knowledge of MLOps/LLMOps.
 * Experience with AWS, Azure, or Google Cloud.
 * Knowledge of databases such as PostgreSQL, MongoDB, or Elasticsearch.
 * Experience with AI safety, guardrails, responsible AI, and data privacy.
 * Experience with PyTorch or TensorFlow.
 * Knowledge of Kubernetes and cloud-native deployments.

Education

 * Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or a related field.

Key Competencies

 * Generative AI & LLMs
 * Python
 * Prompt Engineering
 * RAG
 * AI Agents
 * LangChain / LangGraph
 * Vector Databases
 * NLP
 * Model Evaluation
 * API Development
 * Cloud & MLOps
 * Problem Solving
 * System Design

## Description

We are looking for a Generative AI Engineer to design, develop, and deploy AI-powered applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and machine learning techniques. The ideal candidate should have strong Python programming skills and experience working with modern GenAI frameworks and cloud platforms., * Design and develop Generative AI applications using LLMs and foundation models.

 * Build and deploy LLM-powered applications, AI agents, chatbots, and intelligent automation solutions.
 * Develop RAG pipelines using document processing, embeddings, vector databases, and semantic search.
 * Work with models such as GPT, Claude, Gemini, Llama, and other open-source or proprietary LLMs.
 * Develop effective prompt engineering strategies and evaluate LLM responses.
 * Implement model integration using APIs and SDKs.
 * Fine-tune or customize models for specific business use cases when required.
 * Build AI workflows using frameworks such as LangChain, LangGraph, LlamaIndex, or similar technologies.
 * Develop APIs and backend services using Python, FastAPI, or Flask.
 * Integrate AI solutions with databases, enterprise applications, and cloud services.
 * Implement evaluation frameworks to measure accuracy, relevance, hallucination, latency, and cost.
 * Monitor and optimize AI applications in production.
 * Collaborate with product managers, software engineers, data scientists, and QA teams.
 * Ensure responsible, secure, and scalable implementation of GenAI solutions.