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Architecture & Engineering Specialist/Lead - ML Engineering
Job Description
ZS's Scaled AI practice is part of ZS's rich and advanced AI ecosystem, in the Architecture & Engineering Expertise Center, focused on creating continuous business value for clients using a range of innovative machine learning, deep learning, and engineering capabilities. Being part of Scaled AI practice allows you to collaborate with data scientists to create state-of-the-art AI models, create and use cutting-edge ML platforms, create and deploy advanced ML pipelines and manage the complete ML lifecycle.
Responsibilities
- 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.
- Provide technical expertise, including the evaluation of different products in the 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.
- Lead the 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.
- Perform design/code reviews of the team to identify issues/risks and ensure robustness.
- Drive estimation of technical components and track the 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 exploration to drive innovations/POCs on niche technologies and architecture patterns.
- Systematically debug code issues using stack traces, logs, monitoring tools, and other resources.
Qualifications
- 4-8 years of experience in deploying and productionizing ML models at scale.
- Strong knowledge in developing RAG-based pipelines using frameworks like LangChain and 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 platforms like SageMaker, Azure ML, MLflow, Kubeflow, or other platforms.
- Experience in building ML pipelines and 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, or GCP.
- Strong fundamentals of machine learning and deep learning.
- Up to date with recent developments in machine learning and 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, including documentation, presentations, and discussions.
- Good communicator with clear, concise, active listening and empathy skills.
- Collaborates well with teams of different backgrounds, expertise, and functions.
📌 Job Details
Experience4-8 years
Location
GurgaonPuneBengaluru
CountryIndia
CategoryTechnology
Job TypeFull Time
SalaryNot Disclosed
Posted22-02-2025
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