GenAI Specialist

The GenAI Specialist at Beinex drives innovation by designing and implementing advanced generative AI models to meet client needs. Key responsibilities include fine-tuning LLMs, optimizing scalable ML pipelines, and integrating AI into client applications for seamless performance. The role involves collaborating with stakeholders, communicating complex AI concepts, and staying updated on industry trends to ensure cutting-edge solutions.

Qualifications

Master’s degree in Data Science, Statistics, Engineering or a related field (applicants with a Bachelor’s degree and relevant work experience can also apply)

Responsibilities

Develop Next-Gen Generative AI Models : Research, design, and fine-tune state-of-the-art models like Llama, GPT, Stable Diffusion, and VAEs to drive business impact.

Enhance AI Capabilities : Implement and optimize LLM fine-tuning, prompt engineering, and AI safety mechanisms to improve model efficiency and usability.

Integrate AI into Products / Services : Work closely with business and engineers to embed generative AI solutions into real-world applications, ensuring seamless user experiences.

Optimize for Scale & Performance : Build and refine scalable ML pipelines for efficient model deployment, inference, and real-time adaptation.

Stay Ahead of AI Trends : Keep pace with emerging technologies, model architectures, and industry breakthroughs to drive continuous innovation.

Communicate complex AI concepts to technical and non-technical audiences, ensuring alignment with business strategies.

Key Skills Requirements

At least 4-5 years of experience in Data Science domain with strong exposure to Generative AI.

Experience using Machine and Deep Learning languages, preferably Python and R, to manipulate data and draw insights from large data sets.

Hands-on experience with LLMs, multimodal AI, and diffusion models for applications in text, image, or speech generation.

Strong proficiency in TensorFlow, PyTorch, Hugging Face Transformers, and OpenAI APIs and custom fine tuning of LLMs and AIops toolkits.

Familiarity with cloud platforms (AWS, GCP, Azure), model deployment strategies, and GPU optimization.

Experience building data pipelines and data centric applications using distributed storage platforms in a production setting

Ability to work with large-scale datasets, vector embeddings, and retrieval-based AI systems.