Generative AI Engineer

Job Summary

We are looking for an experienced Generative AI Engineer to design, develop, and deploy production-grade AI applications and LLM-powered solutions. The ideal candidate should have strong hands-on experience in Python, LLMs, RAG, embeddings, vector databases, AI agents, multimodal AI, Gen AI frameworks, and cloud platforms.

Key Responsibilities

• Design and develop end-to-end Generative AI and LLM pipelines.
• Build production-grade applications using LLMs, RAG, AI Agents, and Multimodal AI.
• Work with commercial and open-source LLMs such as GPT, LLaMA, Claude, and Mistral.
• Develop RAG pipelines involving document processing, chunking, embeddings, semantic search, reranking, and retrieval.
• Implement vector database solutions using FAISS, Pinecone, Weaviate, Milvus, or equivalent technologies.
• Develop GenAI applications using LangChain, LlamaIndex, Hugging Face Transformers, or equivalent frameworks.
• Implement prompt engineering, prompt optimization, model evaluation, and fine-tuning.
• Develop and integrate AI services using REST APIs, microservices, and scalable architectures.
• Optimize LLM applications for latency, performance, scalability, reliability, and cost.
• Deploy AI workloads using Docker, Kubernetes, CI/CD pipelines, and cloud platforms.
• Work with Azure AI Foundry and Azure AI Search.
• Develop multimodal AI solutions involving text, image, audio, and speech.
• Implement MLOps / LLMOps practices for monitoring, evaluation, deployment, and versioning.
• Collaborate with Data Science, Product, and Engineering teams to develop enterprise AI solutions.

Required Skills

• 4-8 years of relevant experience in AI/ML, Data Science, or Generative AI Engineering.
• Strong hands-on experience with Python.
• Strong experience with Generative AI and Large Language Models (LLMs).
• Hands-on experience implementing RAG architectures.
• Experience with LangChain, LlamaIndex, or equivalent LLM orchestration frameworks.
• Strong understanding of embeddings, semantic search, and vector databases.
• Experience with FAISS, Pinecone, Weaviate, Milvus, or equivalent.
• Knowledge of PyTorch or TensorFlow.
• Experience with Hugging Face Transformers and open-source LLMs.
• Understanding of prompt engineering and LLM evaluation.
• Experience with at least one cloud platform: AWS / Azure / GCP.
• Exposure to Azure AI Foundry and Azure AI Search.
• Experience with REST APIs, Docker, Kubernetes, and CI/CD.
• Understanding of MLOps / LLMOps practices.

Good to Have

• Experience with Stable Diffusion, DALL•E, Midjourney, or similar image-generation models.
• Experience with multimodal AI involving text, image, audio, and speech.
• Experience optimizing LLM applications for latency, throughput, scalability, and cost.
• Experience deploying GenAI solutions in enterprise environments.

Candidate Profile

Candidates with strong hands-on experience in Generative AI, LLMs, RAG, AI Agents, Vector Databases, Python, and cloud-based AI deployments are encouraged to apply.

Keywords: Generative AI, GenAI, LLM, LLM Engineer, AI Engineer, RAG, LangChain, LlamaIndex, Hugging Face, Python, Vector Database, FAISS, Pinecone, Weaviate, Milvus, Azure AI, Azure AI Foundry, Azure AI Search, AI Agents, MLOps, LLMOps, Multimodal AI, PyTorch, TensorFlow.