- Experience & Education
- Total Experience: Minimum of 4+ years in Data Science and Analytics.
- Specialized Experience: Minimum 3+ years focused specifically on developing generative models (LLMs, GANs, VAEs, Diffusion models) and Deep Learning.
- Education: Bachelor’s or Master’s Degree in Computer Science, Mathematics, Applied Statistics, or a related field.
Technical Skills
Core AI/ML: Expertise in Computer Vision and NLP, with specific experience handling multimodal data (images, text, audio).
GenAI Stack: Familiarity with Gemini, OpenAI GPT-x, open-source LLMs (Llama, Mistral, etc.), and experience in fine-tuning models (PEFT, LoRA). • Retrieval-Augmented Generation (RAG): Expertise in designing RAG architectures to ground LLM responses in proprietary data.
Vector Databases: Proficiency with vector stores such as Pinecone, Milvus, Weaviate, or ChromaDB for efficient semantic search and retrieval.
Prompt Engineering: Advanced skills in prompt optimization, chain-of-thought reasoning, and framework usage (e.g., LangChain, LlamaIndex).
Programming: Strong programming skills in Python with proven ability to write clean, production-ready code.
Frameworks: Proficiency with deep learning frameworks such as PyTorch or TensorFlow.
Cloud: Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP).
Soft Skills
Communication: Excellent fluency in English (B2+ or higher) with the ability to explain complex technical concepts to non-technical audiences.
Problem Solving: A strategic thinker capable of devising holistic solutions for complex customer issues.