Lead/Sr. Data Scientist

We are seeking an innovative Applied Generative AI Engineer to design, develop, and deploy cutting-edge solutions using Large Language Models (LLMs) and other generative AI techniques. This role is pivotal in transforming business processes by integrating advanced conversational AI, vision, and document processing capabilities into our enterprise products.

Key Result Areas (KRAs) & Core Responsibilities

• Generative AI Solution Development:
• Design and implement end-to-end LLM and Small Language Model (SLM)-based applications, specializing in complex business workflows like advanced text generation and code summarization.
• Develop and manage Retrieval-Augmented Generation (RAG) systems for grounding LLMs in proprietary enterprise data, ensuring high-quality, relevant, and verifiable outputs.
• Utilize orchestration frameworks like Lang Chain or Llama Index to build sophisticated, multi-step AI agents and chains.
• Model Optimization and Customization (Fine-Tuning):
• Conduct fine-tuning and other Parameter-Efficient Fine-Tuning (PEFT) techniques (e.g., LoRA) on open-source models to adapt them to specific domain tasks and optimize for latency and throughput.
• Perform rigorous open-source model validation and benchmarking to select the most efficient and performant models for deployment.
• Multimodal & Perception Systems:
• Implement and integrate Computer Vision capabilities, specifically OCR (Optical Character Recognition) and advanced image processing techniques, to extract and structure information from documents and visual data for LLM ingestion.
• Work on multi-modal Generative AI applications (e.g., text-to-image, vision-language models).
• Production & MLOps:
• Deploy, monitor, and manage models in production environments, ensuring scalability and low latency.
• Adhere to MCP (Model Card/Continuous Monitoring/Prompt Governance) principles for responsible and observable AI deployment.
• Implement robust prompt engineering strategies to maximize model performance and maintain ethical guardrails.

Essential Skills & Qualifications (The Modern AI Stack)

Required Technical Skills

• Deep expertise in Large Language Models (LLM) and Small Language Models (SLM) architectures (e.g., Transformer, Mistral, Llama, GPT series).
• Proven ability to implement and optimize Retrieval-Augmented Generation (RAG) systems.
• Hands-on experience with LLM orchestration frameworks (LangChain, LlamaIndex, etc.).
• Strong proficiency in LLM fine-tuning techniques (Supervised Fine-Tuning, RLHF, PEFT/LoRA).
• Experience with model validation and benchmarking of open-source models.
• Expertise in Python programming and related AI/ML libraries (PyTorch, Hugging Face ecosystem).
• Experience with specialized systems like OCR and image processing for data extraction and preparation.
• Familiarity with cloud platforms (AWS, Azure, or GCP) for AI/ML deployment.
• Experience with MLOps practices specific to LLMs (e.g., model serving, prompt versioning).
• Knowledge of vector databases (Pinecone, ChromaDB, Weaviate, etc.) for RAG systems.
• Experience in designing conversational AI interfaces and APIs.
• Understanding of Model Card Practices (MCP) and Responsible AI frameworks.