Posted on: 30/08/2025
Key Responsibilities :
- Design, develop, and deploy LLM-powered AI assistants utilizing OpenAI, Llama, Gemini, or custom fine-tuned models to enhance conversational AI capabilities.
- Implement Retrieval-Augmented Generation (RAG) to seamlessly integrate enterprise knowledge into GPT-based responses, ensuring accuracy and contextual relevance.
- Collaborate with architects to optimize scalable APIs, vector databases, and cloud-native architectures, ensuring efficient AI model deployment and integration.
- Develop robust AI pipelines using LangChain, Hugging Face, and OpenAI SDKs to streamline AI workflows and enhance model performance.
- Deploy AI-driven applications on Google Cloud Platform (GCP), leveraging Vertex AI, BigQuery ML, Cloud Functions, Kubernetes, and Docker for scalability and reliability.
- Ensure high code quality, security, and performance by implementing best practices in DevOps and MLOps, maintaining efficiency across AI development lifecycles.
- Implement and maintain robust CI/CD pipelines to ensure efficient and reliable software delivery (e.g., automated build, test, and deployment processes).
WHO ARE WE LOOKING FOR :
- Technical Expertise : Strong proficiency in LangChain, Hugging Face, Transformers, or Generative AI frameworks to build and optimize AI models.
- Programming Skills : Expertise in Python (mandatory), with optional experience in Node.js or C++.
- Cloud Proficiency : Hands-on experience with Google Cloud Platform (GCP) (preferred) or other cloud platforms like AWS or Azure.
- Database & API Experience : Strong knowledge of REST APIs, SQL/NoSQL databases, and vector databases for scalable AI applications.
- DevOps & MLOps : Practical experience in CI/CD, DevOps, and microservices architectures, including tools like Docker, Kubernetes, GitHub Actions, and Jenkins.
- Soft Skills : Demonstrated ownership, collaboration, and adaptability in a dynamic, fast-paced work environment.
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