Generative AI Engineer
Job Description
Role: Generative AI Engineer
Experience: 5+ Years
Job Summary
We are looking for experienced AI Engineers with hands-on expertise in Generative AI, Large Language Models (LLMs), and AI application development to build and deploy scalable, production-ready AI solutions.
Key Responsibilities
- Design and develop GenAI applications using LLMs such as Claude and GPT.
- Build LLM-powered solutions, including AI assistants, chatbots, and intelligent automation workflows.
- Develop prompt engineering strategies and optimize model responses.
- Integrate LLM APIs into enterprise applications and business workflows.
- Implement Retrieval-Augmented Generation (RAG) solutions and vector database integrations.
- Evaluate model performance, accuracy, and response quality.
- Collaborate with cross-functional teams to deliver scalable AI solutions.
Required Skills
- 5+ years of relevant experience in AI Engineering, GenAI, or related roles.
- Hands-on experience with Claude (Anthropic) and GPT/OpenAI models.
- Strong understanding of LLMs, Generative AI, and prompt engineering.
- Experience with Python and API integrations.
- Knowledge of RAG, embeddings, and vector databases.
- Experience developing and deploying production-grade AI applications.
- Strong analytical and problem-solving skills.
More Info
Key Skills
About Company
Skills:
Tensorflow, Pytorch, Azure, Python, AWS, LangChain, multi-agent systems, Generative AI, RAG architecture, LLMs, Hugging Face, cloud platforms, vector databases, machine learning libraries, LangGraph, Agentic AI, OpenAI APIs
Skills:
Natural Language Processing (NLP), Large Language Models (LLMs), Machine Learning, Deep Learning, Gcp, Azure, Python, AWS, AutoGen, Lang Graph, Lang Chain, OpenAI Assistants API
Skills:
Machine Learning, Tensorflow, Nlp, Pytorch, Terraform, Scrum, Python, Azure DevOps, Agile Methodologies, Jira, Big Data Technologies, Databricks, data visualization tools, Meta, scikit-learn, HuggingFace, machine learning services, non-relational datastores, cloud-based analytics, Spark MLlib, Kanban, nemo, Generative AI, Document Intelligence, Apache MXnet, Azure cloud platforms, open-source tools for data processing