Job Summary
Serve as an Architect in a hybrid work model to design and optimize end to end AI and machine learning platforms on Google Cloud using feature stores MLFlow ML Ops practices Docker and Kubernetes for large scale enterprise solutions. Apply deep expertise in artificial intelligence machine learning concepts and automation to build reliable pipelines drive model lifecycle governance and support telecom or billing and revenue management initiatives while collaborating across product engineer
Responsibilities
- Design scalable AI and machine learning platform architectures that integrate feature store capabilities model training workflows and prediction services to support complex enterprise use cases in a hybrid work model.
- Develop robust ML Ops frameworks that standardize experiment tracking versioning and deployment workflows across MLFlow Google Vertex AI and Git based repositories to ensure traceability and reproducibility of all models.
- Configure and maintain CI and CD pipelines using Jenkins to automate building testing and releasing machine learning services and supporting applications with a strong focus on reliability and rapid iteration.
- Implement orchestrated data and model pipelines using Airflow to manage dependencies schedules and monitoring across data ingestion feature engineering training evaluation and production serving stages.
- Architect container based solutions using Docker and Kubernetes to enable portable resilient and scalable deployment of data processing jobs model serving endpoints and supporting microservices across Google Cloud environments.
- Apply advanced AI and machine learning concepts to select appropriate algorithms design feature engineering strategies and define evaluation approaches that align with business objectives and ethical considerations.
- Collaborate closely with data scientists data engineers and application teams to translate business requirements from telecom or billing and revenue management domains into cohesive technical designs and implementation roadmaps.
- Establish governance practices for machine learning including model monitoring performance drift detection audit readiness and documentation so that solutions remain trustworthy compliant and maintainable over time.
- Use Terraform to define cloud infrastructure for data platforms and ML environments as code enabling repeatable provisioning configuration consistency and secure operations on Google Cloud machine learning services.
- Guide optimization of end to end pipelines by analyzing resource usage latency and failure patterns then proposing architectural improvements that enhance efficiency scalability and cost effectiveness.
- Coordinate hybrid working practices by using collaborative tools clear documentation and standardized processes so that distributed teams can build operate and enhance AI solutions effectively during day shift hours.
- Ensure security privacy and responsible use of data in all AI and machine learning designs by applying organizational policies industry standards and careful access control across platforms and tools.
- Communicate architecture decisions technical tradeoffs and roadmap implications to stakeholders in concise terms highlighting how the AI and machine learning ecosystem advances company goals and societal impact.
Qualifications
- Demonstrate extensive hands on experience with feature store implementations MLFlow and ML Ops methodologies including experiment tracking model lifecycle management and automated deployment practices.
- Show strong proficiency in Jenkins Airflow and Git for building integrated CI and CD workflows orchestrating complex data pipelines and maintaining clean version control across code and configuration assets.
- Exhibit advanced skills in Docker Kubernetes and Terraform for designing containerized workloads managing cluster resources and codifying cloud infrastructure particularly in Google Cloud machine learning contexts.
- Apply deep knowledge of artificial intelligence machine learning theory and practical modeling techniques to design solutions that deliver measurable business outcomes and reliable predictive performance.
- Preferably bring exposure to telecom and billing and revenue management domains with ability to understand rating charging and revenue flows and to design AI solutions that improve accuracy and operational efficiency.
- Leverage strong problem solving capabilities analytical thinking and clear communication skills to collaborate across cross functional teams in a hybrid model while maintaining high standards of quality and accountability.
About Cognizant:
Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization’s unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant.
Additional employment information
Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview.
Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, state or local laws.
