Architecture & Engineering Specialist/Lead - ML Engineering

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

Role: Data Science & Machine Learning - Other

Industry Type: Analytics / KPO / Research

Department: Data Science & Analytics

Employment Type: Full Time, Permanent

Role Category: Data Science & Machine Learning

Education

UG: Any Graduate

PG: CS in Any Specialization

Key Skills

Skills highlighted with '' are preferred keyskills

ML Engineering

KubeflowAzureOpen AIML pipelinesGCPdata securityMLFlowDevOps CI / CDAWS