Job
Description
What Youll Do Work on ZS AI Products or client AI solutions using ML Engineering & Data Science Tech stack Work on creating GenAI applications such as answering engines, extraction components, and content authoring Provides 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 Design and build advanced ML pipelines for Feature engineering, inferencing and continuous model training Design and build ML Ops framework and components for model visibility and tracking Manage the complete ML lifecycle Leads team to achieve established goals, such as delivering new features or functionality Mentor and groom technical talent within the team Review individual work plans before implementation to identify potential issue areas and/or reduce rework Performs design / code reviews of the team to identify issues/risks and ensure robustness Drive estimation of technical components and tracks team's progress Handle client interactions as and when required Recommend designs that are scalable, testable, debuggable, robust, maintainable and usable Maintain a culture of rapid learning and explorations to drive innovations / POCs on niche technologies and architecture patterns; Systematically debug code issues using stack traces, logs, monitoring tools and other resources What Youll Bring 4-8 years experience in deploying and productionizing ML models at scale Strong knowledge in developing RAG-based pipelines using frameworks like LangChain & LlamaIndex Good understanding of various LLMs like Azure OpenAI and proficiency in their effective utilization Solid working knowledge of the engineering components essential in a Gen AI application, including Vector DB, caching layer, chunking, and embedding Experience in scaling GenAI or similar applications to accommodate a high number of users, large data size, and reduce response time. Expertise in Designing, configuring and using ML Engineering platform like Sagemaker, Azure ML, MLFlow, Kubeflow or other platforms Experience in Building ML pipelines, Troubleshooting ML models for high performance and scalability Experience with Spark or other distributed computing frameworks Strong programming expertise in Python, Scala or Java Experience in deployment to cloud services like AWS, Azure, GCP Strong fundamentals of machine learning and deep learning Up to date with recent developments in Machine learning and are familiar with current trends in the wider ML community Knowledgeable of core CS concepts such as common data structures and algorithms Excellent technical presentation skills (documentation, presentations, discussions) Good communicator with clear and concise, active listening and empathy skills. Collaborate well with teams with different backgrounds / expertise / functions Additional Skills: Understanding DevOps CI / CD, data security, experience in designing on cloud platform; Willingness to travel to other global offices as needed to work with the client or other internal project teams
