LLM Development & Optimization
Fine-tune and optimize foundation models (e.g., GPT, LLaMA, Mistral, Claude) for specific business and domain applications.1
Build scalable pipelines for model training, evaluation, and deployment (MLOps/LLMOps).1
Conduct performance tuning, benchmarking, and continuous improvement of model accuracy and efficiency.1
Prompt Engineering & Evaluation
Develop and refine advanced prompting strategies (structured, few-shot, chain-of-thought, tool-augmented prompts).1
Design automated evaluation frameworks for response quality, consistency, and factual accuracy.1
Agentic AI & System Design
Architect intelligent, autonomous, or semi-autonomous AI agents that can reason, plan, and execute tasks using contextual knowledge.1
Integrate external APIs, vector databases, and retrieval systems to enhance model reasoning and context handling.1
Data Strategy & Engineering
Curate, preprocess, and manage large-scale datasets for generative AI model training and fine-tuning.1
Ensure data quality, ethical use, and compliance with data governance standards.1
Research & Innovation
Stay up to date with advancements in LLMs, RAG, multi-agent systems, and multimodal AI.1
Prototype and experiment with emerging GenAI technologies to identify potential use cases and improvements.1
Required Skills & Qualifications
Bachelor s or Master s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.1
5-7 years of experience in Data Science or Machine Learning, with at least 2-3 years in Generative AI / LLM-based projects.1
Proficiency in Python, with strong knowledge of libraries and frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, LangChain, or LlamaIndex.1
Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate) and AI orchestration frameworks.1
Strong understanding of machine learning workflows, data pipelines, and model deployment (MLOps / LLMOps) on AWS, Azure, or GCP.1
Preferred Qualifications
Experience building or integrating agentic AI systems or multi-agent orchestration frameworks (e.g., CrewAI).1
Exposure to multimodal AI systems (text, image, and audio).1
Knowledge of AI safety, interpretability, and ethical AI principles.1
Contributions to AI research, open-source projects, or technical publications