Sr AIML/GenAI Lead/Architect

We are seeking a highly skilled and versatile Senior AIML/GenAI Lead who embodies the rare combination of a strong software engineer, a pragmatic data scientist, and an expert in building robust, scalable ML applications. Experience in Gen AI, LLM, ML/DL/NLP, RAG, Lang chain, Mistral, Llama, Hugging Face, Python, Tensorflow, Pytorch, Django, Vector DB. This role is critical to our mission, bridging the gap between cutting-edge ML research and robust, production-ready systems. You will be instrumental in designing, developing, deploying, and maintaining our core AI-powered products and features. This demands a blend of analytical rigor, architectural foresight, and a deep understanding of the entire machine learning lifecycle, from data exploration and model development to deployment, monitoring, and continuous improvement. If you thrive on taking ML models from concept to customer impact and possess exceptional software design skills, we encourage you to apply.

: • ML Model Development & Optimization: Experience in developing and implementing generative AI models and algorithms. Collaborate with Data Scientists to understand business problems, explore data, develop, train, and evaluate machine learning models (e.g., supervised, unsupervised, deep learning, reinforcement learning). Optimize models for performance, efficiency, and interpretability. • End-to-End ML Application Development: Lead the design, development, and deployment of machine learning models and intelligent systems into production environments, ensuring they are robust, scalable, and performant. • Software Design & Architecture: Apply strong software engineering principles to design and build clean, modular, testable, and maintainable ML pipelines, APIs, and services. Contribute significantly to the architectural decisions for our ML platform and applications. • Data Engineering for ML: Design and implement data pipelines for feature engineering, data transformation, and data versioning to support ML model training and inference. • Performance & Scalability: Identify and resolve performance bottlenecks in ML systems. Ensure the scalability and reliability of deployed models under varying load conditions. • Collaboration & Mentorship: Work closely with cross-functional teams including Data Scientists, Software Engineers, Product Managers, and DevOps to integrate ML solutions seamlessly into our products. Potentially mentor junior engineers on best practices in ML engineering and software design. • Research & Innovation: Stay abreast of the latest advancements in machine learning, MLOps, and related technologies. Propose and experiment with new techniques and tools to improve our ML capabilities. • Documentation: Create clear and comprehensive documentation for ML models, pipelines, and services.