On-Site
Full-Time
Hyderabad, Telangana
India
About the Role
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:
• Experience: Minimum 4+ years in AI development, with at least 2+ year of hands-on
experience in Generative AI for Natural Language Processing (NLP) or Computer Vision.
• 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, fastpaced
work environment.
• 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:
• Experience: Minimum 4+ years in AI development, with at least 2+ year of hands-on
experience in Generative AI for Natural Language Processing (NLP) or Computer Vision.
• 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, fastpaced
work environment.