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Job Description
Position Overview:
- We are looking for an accomplished Generative AI Engineer with 2 to 5 years of experience in the design, development, and deployment of AI-driven applications.
- The successful candidate will possess practical expertise in LLMs, RAG, Agentic AI, Prompt Engineering, Python, LangChain, Vector Databases, and AI Model Integration.
Core Responsibilities:
- Create and implement Generative AI solutions utilizing Large Language Models (LLMs).
- Develop and enhance RAG (Retrieval Augmented Generation) pipelines.
- Construct AI Agents using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or similar technologies.
- Incorporate OpenAI, Azure OpenAI, Claude, Gemini, Llama, or other foundational models into enterprise-level applications.
- Apply prompt engineering and fine-tuning techniques to enhance AI output quality.
- Design APIs and microservices to support AI functionalities.
- Engage with vector databases like Pinecone, ChromaDB, FAISS, Weaviate, or Milvus.
- Collaborate with cross-functional teams to translate business and technical requirements into AI solutions.
- Optimize the performance, scalability, security, and cost-efficiency of models.
- Conduct testing, validation, and deployment of AI systems.
Technical Qualifications:
- Proven experience with Generative AI, LLMs, and Prompt Engineering.
- Familiarity with frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or Semantic Kernel.
- Understanding of RAG Architecture and Vector Databases.
- Experience with Azure OpenAI, OpenAI APIs, Gemini, Claude, Llama, or Hugging Face models.
- Solid grasp of NLP, Machine Learning, and Deep Learning principles.
- Experience in working with REST APIs and microservices architecture.
- Proficient with Git, Docker, and CI/CD practices.
- Experience with cloud infrastructure, including Azure, AWS, or GCP.
Additional Qualifications:
- Knowledge of MCP (Model Context Protocol).
- Experience with multi-agent systems and Agentic AI workflows.
- Familiarity with fine-tuning embeddings and model evaluation techniques.
- Experience in Kubernetes and MLOps methodologies.
- Understanding of AI governance, security, and responsible AI practices.