AI Engineer

• Build and optimize applications using LLMs (OpenAI, Anthropic, Llama, Mistral, Gemini, etc.).
• Fine-tune, prompt-tune, or RAG-augment models to improve domain accuracy and performance.
• Implement Retrieval-Augmented Generation (RAG) pipelines using vector stores such as Pinecone, Weaviate, Chroma, Redis, or Elastic.
• Develop and maintain GenAI microservices and APIs for integration with enterprise apps.
• Design, develop, and implement GenAI-driven solutions using CoPilot, Copilot Studio, Microsoft Power Platforms, Power Apps and Power Automate.
• Develop and maintain Copilot agents to support GenAI use cases.
• Integrate AI solutions with existing systems and third-party applications, services, and APIs.
• Develop clean, scalable, production-grade services in Python, JavaScript/TypeScript, or Java.
• Build backend APIs using frameworks like FastAPI, Flask, Node.js, Django.
• Use modern CI/CD and DevOps practices to deploy cloud-native AI services (AWS, Azure, GCP).

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