Junior Engineer

We are looking for an experienced AI/ML Engineer with over 4 years in the field of AI/ML to become a part of our team. The perfect candidate should possess a solid background in traditional machine learning, Generative AI, and have experience fine-tuning large language models (LLMs). This role involves a significant contribution to designing scalable AI solutions that enhance business operations. You will collaborate with cross-functional teams to develop AI-driven systems that offer substantial operational benefits. A strong understanding of Retrieval-Augmented Generation (RAG) and proficiency with databases are essential for this role.

• Experience: 4 to 10 years in AI/ML, with a strong emphasis on traditional machine learning and deploying AI/ML models in production environments.
• Generative AI: Demonstrated experience in developing applications based on Generative AI for real-world users.
• Machine Learning: Solid foundation in machine learning techniques, including both deep learning and traditional ML methods.
• Language Models: Experience in fine-tuning large language models (LLMs) and applying Retrieval-Augmented Generation (RAG) for business purposes.
• Data Management: Proficiency with databases, including SQL and NoSQL, and managing large datasets in production settings.
• Technical Expertise: Expertise in AI/ML frameworks such as TensorFlow, PyTorch, Hugging Face, and programming languages like Python.
• MLOps: Knowledge of MLOps practices for deploying, monitoring, and maintaining machine learning models at scale.
• Cloud and DevOps: Familiarity with cloud platforms such as AWS, GCP, or Azure, and tools like Docker and Kubernetes.
• Communication: Strong interpersonal and communication skills, capable of working effectively with both technical and non-technical teams.

• Design, develop, and deploy AI applications suitable for production that seamlessly integrate into business workflows, utilizing Generative AI models like GPT and Retrieval-Augmented Generation (RAG) frameworks.
• Lead the complete machine learning lifecycle, encompassing data collection, preprocessing, model development, fine-tuning, deployment, and ongoing monitoring.
• Collaborate with product and engineering teams to embed AI/ML solutions into workflows, driving automation and enhancing productivity in professional services.
• Apply traditional machine learning techniques, such as regression, classification, and clustering, to improve existing solutions and create new ones.
• Fine-tune large language models (LLMs) and implement RAG strategies to deliver real-time, data-driven insights and support decision-making processes.
• Work with databases to underpin data-driven AI models, ensuring efficient data storage, access, and preprocessing of large datasets.
• Continuously optimize model performance to maintain scalability, reliability, and efficiency in a cloud-based environment.
• Monitor and sustain production models, employing feedback loops and A/B testing to enhance performance.
• Stay current with the latest advancements in AI/ML and integrate them into AI solutions.
• Mentor and guide junior data scientists, fostering a collaborative and innovative culture.