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3.3

based on 2.2k Reviews

152 ZS Jobs

Architecture & Engineering Specialist/Lead - ML Engineering

ZS Associates

3.3

based on 2.2k Reviews

4-8 years

Pune, Bangalore / Bengaluru

1 vacancy

Architecture & Engineering Specialist/Lead - ML Engineering

posted 3mon ago

Job Role Insights

Flexible timing

Key skills for the job

Python Cloud Computing Architecture Machine Learning Analytical Chemistry Deep Learning

+ 4 more

Job Description

Our most valuable asset is our people .

At ZS we honor the visible and invisible elements of our identities, personal experiences and belief systems the ones that comprise us as individuals, shape who we are and

make us unique. We believe your personal interests, identities, and desire to learn are part of your success here. Learn more about our diversity, equity, and inclusion efforts and the networks ZS supports to assist our ZSers in cultivating community spaces, obtaining the resources they need to thrive, and sharing the

Architecture Engineering Specialist ML Engineering

ZS s India Capability Expertise Center (CEC) houses more than 60% of ZS people across three offices in New Delhi, Pune and Bengaluru. Our teams work with colleagues across North America, Europe and East Asia to create and deliver real world solutions to the clients who drive our business. The CEC maintains standards of analytical, operational and technological excellence across our capability groups. Together, our collective knowledge enables each ZS team to deliver superior results to our clients.

ZSs Scaled AI practice is part of ZSs 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.

What You ll 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 teams 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 You ll 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