Overview

## Full Description

Responsibilities

• fxis.ai, established in 2015 and headquartered in Ahmedabad, India, is a pioneering leader in Artificial Intelligence (AI) and Data Science. The company specializes in delivering innovative AI solutions, including Machine Learning, Generative AI, Natural Language Processing (NLP), and Full Stack Development, to a global clientele.
• With a dedicated team of over 85 highly skilled developers, FXIS.ai has successfully completed more than 400 projects for over 250 distinguished clients, including industry giants like Johnson & Johnson, GMR Group, and Paravision. Their expertise spans a wide range of advanced technologies, such as chatbots, reinforcement learning, deep learning, neural networks, and computer vision.
• FXIS.ai's commitment to excellence and innovation has positioned them as a significant force in the AI industry, influencing 10% of the global market. Their tailored solutions have been instrumental in driving efficiency and innovation across various sectors, including healthcare, gaming, fintech, cybersecurity, and real estate.
• For more information, visit their official website at fxis.ai.
• About fxis.ai:
• Job Overview:
• We are seeking an AI Engineer (Level 2) with a deep passion for exploring AI technologies, particularly Large Language Models (LLMs) and advanced NLP techniques. The ideal candidate will have a strong foundation in deep learning architectures and experience with state-of-the-art AI frameworks. In this role, you will work on developing and deploying intelligent applications that solve real-world problems for our clients, leveraging tools like LangChain, Hugging Face, and OpenAI APIs.
• Design and Develop AI Solutions: Build advanced AI and NLP solutions focusing on large language models. Experiment with state-of-the-art Transformer architectures (e.g., BERT, GPT, LLaMA) to address complex language understanding and generation tasks.
• LLM Implementation: Leverage modern AI frameworks and tools (such as LangChain, Hugging Face Transformers, OpenAI APIs, PyTorch, TensorFlow) to develop, fine-tune, and integrate LLMs into applications. Ensure models are optimized and efficient for production use.
• Advanced NLP Techniques: Implement and enhance NLP capabilities like text generation, summarization, and retrieval-augmented generation (RAG) to provide intelligent and contextually aware features in our solutions.
• Prompt Engineering & Fine-Tuning: Craft effective prompts and fine-tune LLMs to improve model performance for specific domains and use cases. Experiment with prompt strategies and training techniques to optimize generative models.
• Production Deployment & MLOps: Deploy and scale models in production environments, ensuring reliability and low-latency performance. Implement MLOps best practices (CI/CD pipelines, model monitoring, version control) to achieve robust, repeatable model deployments.
• Cloud AI Integration: Utilize AI/ML cloud services (AWS, GCP, Azure) for model training, deployment, and infrastructure management. Optimize cloud resources to handle large-scale training and inference efficiently.
• Model Optimization & Scaling: Continuously optimize model performance and implement scaling strategies to handle large datasets and high-volume usage.
• Cross-Functional Collaboration: Work closely with data scientists, software engineers, and product managers to integrate AI capabilities into products and solutions. Collaborate in cross-functional teams to gather requirements, brainstorm ideas, and ensure AI systems meet client needs and quality standards.
• Innovation and Research: Stay up-to-date with the latest AI research, papers, and trends in the industry. Proactively experiment with new algorithms, models, and techniques to keep our solutions at the cutting edge.

Education

• Deep Learning & LLM Expertise: Strong foundation in deep learning and NLP, with experience in transformer-based architectures and large language models (e.g., BERT, GPT series, LLaMA).
• Programming & Frameworks: Proficiency in Python programming and experience with major AI/ML frameworks and libraries – e.g., PyTorch, TensorFlow, and data libraries like NumPy, Pandas, scikit-learn. Familiarity with NLP/LLM tools such as Hugging Face Transformers, LangChain, and OpenAI APIs is highly desired.
• NLP & Generation: Proven experience developing NLP solutions, including working on text generation, summarization, conversational AI, or retrieval-augmented generation (RAG) systems.
• Model Tuning: Hands-on skills in prompt engineering and fine-tuning LLMs or other deep learning models to adapt them for specific tasks or domains. Experience with transfer learning and hyperparameter tuning for model optimization.
• Deployment & Cloud: Experience deploying machine learning models into production environments. Familiarity with deploying