Job Responsibilities

• Collaborate with product, engineering, and research teams to integrate Generative AI capabilities into applications and services.
• Research and implement cutting-edge advancements in Generative AI and Large Language Models (LLMs).
• Develop and optimize prompt engineering strategies to enhance model outputs.
• Participate in prompt tuning, RLHF, and benchmarking experiments to improve model performance and accuracy.
• Work with vector databases (e.g., FAISS, PGVector, Pinecone) for retrieval-augmented generation (RAG) and semantic search use cases.
• Design scalable, resilient, and observable generative AI pipelines for real-world applications.
• Monitor model performance and drive continuous improvement in efficiency, accuracy, and user experience.

Job Qualifications

• Strong experience in deep learning and NLP, especially with LLMs and generative models.
• Proficiency in Python and ML libraries such as Hugging Face Transformers, LangChain, and OpenAI APIs.
• Hands-on experience with prompt engineering, few-shot learning, and fine-tuning LLMs.
• Familiarity with ML Ops/LLMOps and distributed systems.
• Experience working with vector databases and building RAG pipelines.
• Strong understanding of system design, scalability, observability, and performance tuning.
• Excellent analytical and problem-solving skills.
• Passion for exploring and building with emerging AI technologies.

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