Job_Description":"

Key Responsibilities
Research and implement Generative AI use cases using LLMs (text, voice, image, or multimodal).
Build prototypes using frameworks such as LangChain, LlamaIndex
Design and optimize prompts for accuracy, safety, and performance.
Implement RAG (Retrievalu2011Augmented Generation) pipelines using vector databases.
Integrate AI models via APIs or openu2011source models (OpenAI, Azure OpenAI, Hugging Face, local LLMs).
Work on AI agents , tools, and orchestrations (function calling, workflows).
Evaluate model outputs using metrics like relevance, hallucination rate, latency, and cost.
Assist in deploying GenAI applications using Docker and cloud platforms preferably Azure.
Collaborate with product, data, and engineering teams
Document experiments, findings, and best practices.
Mentor engineers and drive best practices

Requirements

Required Skills
3+ years of professional experience and 2+ years of experience in GenAI.




Solid experience in Python or Java
Hands-on with LLMs, prompt engineering, and RAG
Experience with LangChain, LlamaIndex, or similar
Knowledge of vector databases (FAISS, Pinecone, etc.)
Experience with cloud platforms (Azure preferred)
Understanding of microservices and distributed systems
Basic experience with Git and REST APIs. Ability to read research blogs or documentation and implement ideas quickly.

Benefits

Advantages
Handsu2011on exposure to contemporary GenAI stacks
Certificate / Letter of Recommendation
Access to realu2011world datasets and use cases ","

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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