Artificial Intelligence Developer

Position - AI Engineer As an AI Engineer, you will design, implement, and optimize machine learning models and AI systems to solve complex problems. You will work closely with cross-functional teams to integrate AI solutions into our products and services, ensuring scalability and efficiency. Key Responsibilities: Application Development: Design and develop AI-powered applications using state-of-the-art LLM models and generative AI techniques. Implement scalable solutions that integrate LLM-powered tools into existing workflows or standalone products. Model Optimization: Fine-tune pre-trained LLM models to meet specific application requirements. Optimize model performance for real-time and high-throughput environments. LLMOps Implementation: Develop and maintain pipelines for model deployment, monitoring, and retraining. Set up robust systems for model performance monitoring and diagnostics. Ensure reliable operations through analytics and insights into model behavior. Vector Databases and Data Management: Utilize vector databases for efficient storage and retrieval of embeddings. Integrate databases with LLM applications to enhance query and recommendation systems. Collaboration and Innovation: Work closely with cross-functional teams, including product managers, data scientists, and software engineers. Stay up-to-date with advancements in generative AI and LLM technologies to drive innovation. Skills and Experience 3+ years of experience in AI/ML development, with a focus on generative AI and LLMs. Proficiency in programming languages such as Python and frameworks like PyTorch or TensorFlow. Hands-on experience in fine-tuning and deploying LLM models (e.g., GPT, BERT, etc.). Familiarity with LLMOps practices, including pipeline automation, monitoring, and analytics. Experience with vector databases (e.g., Pinecone, Weaviate, or similar). Strong knowledge of natural language processing (NLP) and machine learning principles. You should certainly apply if: Understanding of MLOps principles and cloud platforms (AWS, GCP, Azure). Familiarity with prompt engineering and reinforcement learning from human feedback (RLHF). Experience in building real-time applications powered by generative AI. Knowledge of distributed systems and scalable architectures.