FACE 2 FACE INTERVIEW: Lead Data Scientist / ML Ops Engineer || Hybrid Irving, TX (Local Only) || Client: CVS || NO H1B/OPT..
Role: Lead Data Scientist / ML Ops Engineer
Location: Irving, TX (Hybrid)
Duration: 6+ Month
MOI: Onsite (FACE 2 FACE)
Client: CVS
Visa: NO H1B/OPT
Day to Day: This person will take the base code model that has been developed by the Data Science team and will be responsible to scale it, deploy it in a more reusable way, and manage the pipeline
Position Summary
As a Lead ML Ops Engineer, you will work on the Retail side of the company and drive the design and implementation of functionality related to the end-to-end ML/AI and Feature lifecycle management on Azure/Google Cloud Platform, leveraging and integrating the cloud native services with other standard operational and automation tools. Once this has been established, you will develop models to support Engineering projects. EML Ops is the expectation of the role. You will be developing models that can help from an Engineering perspective
You will also be responsible for guiding more JR members of the team. 70% individual contributor, 30% reviewing JR-level engineers
Required:
• Python & SQL for scripting & programming
• Knowledge of ML & Ops Engineering
• Open source (but they use Python on Azure Kubernetes)
• ML Ops
• Kubernetes
• Public Cloud
• Support the deployment of ML/AI pipelines on the platform.
• Enable functionality to support analysis, model optimization, statistical testing, model versioning, deployment and monitoring of model and data.
• Ability to translate functionality into scalable, tested, and configurable platform architecture and software.
• Establish strong software engineering principles for development in Python on the Azure/Google Cloud Platform.
• Deliver features aligned to enterprise AutoML, Feature Engineering, and MLOPS capability.
• Innovative thinking and great communication skills.
• Strong ownership of deliverables, with design decisions aligned to scale and industry best practices.
• Provide technical leadership and mentorship to a team of machine learning engineers. Collaborate with cross-functional teams to align ML initiatives with overall business goals.
• Design, implement, and optimize machine learning algorithms and models. Stay abreast of the latest advancements in ML research and apply them to solve complex business problems.
• Architect and implement scalable and efficient machine learning systems. Collaborate with software engineers to integrate ML models into production systems.
• Work closely with data engineers to ensure the availability and quality of data for training and evaluation of machine learning models.
• Develop strategies for deploying machine learning models at scale. Ensure models are integrated into production systems with high reliability and performance.
• Design and conduct experiments to evaluate the performance of machine learning models. Iterate on models based on feedback and evolving business requirements.
Required Qualifications
• 6+ years of experience in analytics domains, and deep understanding of ML operationalization and lifecycle management.
• 5+ years of deploying and monitoring analytical assets in batch/real-time business processes.
• 5+ years of SQL & Python programming experience leveraging strong software development principles.
• Experience in designing and developing AI applications and systems.
• Experience with real-time and streaming technology (i.e. Azure Event Hubs, Azure Functions, Pub/Sub, Kafka, Spark Streaming etc.)
• Experience with REST API/Microservice development using Python/Java.
• Experience with deployment/scaling of apps on containerized environment (AKS and/or GKE)
• Experience with Snowflake/BigQuery, Google Dataproc/Databricks or any big data frameworks on Spark
• Experience with RDBMS and NoSQL Databases and hands-on query tuning/optimization.
Preferred Qualifications
• Hands on experience in building solutions using cloud native services (Azure, GCP preferred)
• Understanding of DevOps, Infrastructure as Code, automation for self service
Education
• Required: Bachelor’s degree in computer science, Engineering, Statistics, Physics, Math, or related field or equivalent experience
• Preferred: Master’s Degree or PhD with coursework focused on advanced algorithms, mathematics in computing, data structures, etc.