Architecture & Engineering Specialist/Lead - ML Engineering

As an Architecture & Engineering Specialist in ML Engineering at ZS, you will be a part of ZS's Scaled AI practice within the Architecture & Engineering Expertise Center. Your role involves collaborating with data scientists to develop cutting-edge AI models, ML platforms, and pipelines to create continuous business value for clients. You will be responsible for managing the complete ML lifecycle, leading a team to achieve established goals, and mentoring technical talent within the team. Additionally, you will drive innovations on niche technologies and architecture patterns to maintain a culture of rapid learning and explorations. Key Responsibilities: - Work on ZS AI Products or client AI solutions using ML Engineering & Data Science Tech stack - Create GenAI applications such as answering engines, extraction components, and content authoring - Provide technical expertise including the evaluation of different products in ML Tech stack - Collaborate with data scientists to create state-of-the-art AI models - Design and build ML Engineering platforms and components - Build advanced ML pipelines for Feature engineering, inferencing, and continuous model training - Develop ML Ops framework and components for model visibility and tracking - Lead the team to achieve established goals and mentor technical talent - Review individual work plans, perform design/code reviews, and drive estimation of technical components - Handle client interactions and recommend scalable, testable, debuggable, and maintainable designs - Maintain a culture of rapid learning and explorations to drive innovations on niche technologies and architecture patterns - Systematically debug code issues using stack traces, logs, and monitoring tools Qualifications Required: - 4-8 years of experience in deploying and productionizing ML models at scale - Strong knowledge in developing RAG-based pipelines using frameworks like LangChain & LlamaIndex - Proficiency in various LLMs like Azure OpenAI and their effective utilization - Experience in scaling GenAI applications to accommodate a high number of users and large data size - Expertise in Designing, configuring, and using ML Engineering platforms like Sagemaker, Azure ML, MLFlow, Kubeflow - Strong programming skills in Python, Scala, or Java - Experience with Spark or other distributed computing frameworks - Knowledge of machine learning, deep learning, and recent developments in the ML community - Understanding of core CS concepts such as common data structures and algorithms - Excellent technical presentation skills and good communicator with clear and concise communication (Note: Additional details of the company were not provided in the job description.),