Senior GenAI Engineer

Location- Remote

Job Summary

We are looking for a Senior GenAI Engineer to design, develop, and deploy AI-powered applications using Generative AI, Large Language Models (LLMs), and modern machine learning techniques. The ideal candidate will have strong hands-on experience building production-grade GenAI solutions and integrating them into business applications.

Key Responsibilities

• Design and develop GenAI applications using LLMs, RAG, prompt engineering, and AI agents.
• Build and optimize production-ready AI/ML solutions using Python and relevant frameworks.
• Integrate models from providers such as OpenAI, Azure OpenAI, Anthropic, or open-source LLMs.
• Develop RAG pipelines using vector databases and embedding models.
• Implement prompt engineering, evaluation, guardrails, and model optimization techniques.
• Work with AI agent frameworks and orchestration tools where appropriate.
• Deploy and monitor GenAI solutions on cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Collaborate with product, engineering, and data teams to translate business requirements into AI solutions.
• Ensure solutions meet requirements for scalability, security, performance, and reliability.
• Stay current with emerging developments in Generative AI, LLMs, and AI engineering.

Required Skills & Experience

• Strong proficiency in Python.
• Hands-on experience with Generative AI and LLMs.
• Strong understanding of RAG, embeddings, vector databases, and prompt engineering.
• Experience with frameworks such as LangChain, LlamaIndex, or similar.
• Experience working with APIs and integrating AI models into applications.
• Good understanding of ML/AI concepts and model evaluation.
• Experience with at least one major cloud platform: AWS, Azure, or Google Cloud Platform.
• Experience with REST APIs, Git, Docker, and CI/CD.
• Strong problem-solving and communication skills.

Preferred Skills

• Experience building AI agents / agentic workflows.
• Experience with fine-tuning or adapting open-source LLMs.
• Familiarity with vector databases such as Pinecone, Weaviate, Qdrant, or FAISS.
• Knowledge of LLM observability and evaluation tools.
• Experience with Kubernetes and production ML/AI deployments.
• Understanding of AI security, responsible AI, and data privacy.