We are seeking a passionate and skilled AI/ML Developer with hands-on experience in Large Language Models (LLMs), Python, and prompt engineering. You will be responsible for designing, developing, and deploying AI-powered solutions using state-of-the-art machine learning models with a focus on language understanding and generation. This role requires a blend of technical depth, logical problem-solving, and strong communication skills to translate complex business requirements into scalable AI solutions. 

Responsibilities: 

• Design, fine-tune, and deploy LLM-based solutions for real-world business problems. 

• Develop and optimize prompts to guide model behavior for various use cases. 

• Write clean, efficient, and scalable Python code for model development and integration. 

• Analyze model performance and troubleshoot issues with data quality, token usage, and output alignment. 

• Collaborate with product owners, data scientists, and engineers to identify and prioritize AI use cases. 

• Stay current with the latest advancements in AI/ML, especially in the generative AI space. 

• Document model behavior, prompt iterations, and implementation decisions clearly for both technical and non-technical stakeholders. 

• Ensure ethical and responsible AI development practices. 

Requirements : 

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field. 

• 2-5 years of proven experience working with LLMs (e.g., GPT, LLaMA, Claude, Mistral) in production or prototyping environments. 

• Strong programming skills in Python, with experience using relevant libraries (e.g., Transformers, LangChain, OpenAI API, Hugging Face) 

• Practical experience in prompt engineering, including prompt chaining, prompt tuning, and system message crafting. 

• Solid foundation in machine learning concepts and natural language processing. 

• Strong logical thinking and problem-solving skills with an ability to break down complex problems into structured solutions. 

• Excellent communication skills – both written and verbal – to convey technical concepts to diverse stakeholders. 

Preferred Qualifications :  

• Experience with model fine-tuning or reinforcement learning from human feedback (RLHF). 

• Familiarity with vector databases and retrieval-augmented generation (RAG) techniques. 

• Exposure to cloud platforms like AWS, Azure, or GCP. 

• Experience with ML Ops, containerization (Docker), and model deployment pipelines. 

• Contributions to open-source AI/ML projects or publications in relevant areas.