Job Summary
Sr Developer role in a global organization focused on building robust data and machine learning pipelines using Python Airflow and modern ML Ops practices in a hybrid work model. The role involves designing scalable workflows ensuring reliable model deployment monitoring performance and collaborating with cross functional teams to deliver impactful analytics solutions while following secure and compliant engineering standards.
Responsibilities
- Design and implement scalable Airflow pipelines that reliably orchestrate complex data workflows to support analytics and machine learning use cases in a hybrid work environment.
- Develop optimize and maintain end to end ML Ops frameworks that streamline model training validation deployment and monitoring across multiple environments for sustainable value delivery.
- Ensure robust data quality checks within Airflow tasks by defining validation rules and automated alerts that prevent downstream issues and improve trust in analytical outputs.
- Collaborate with data scientists and data engineers to translate experimental notebooks into production ready ML pipelines that meet performance reliability and compliance expectations.
- Configure and maintain CI CD processes for ML components by integrating testing packaging and deployment automation that reduces manual effort and accelerates release cycles.
- Implement model versioning and model registry practices that allow transparent tracking of changes reproducibility of experiments and controlled promotion of models into production.
- Monitor running Airflow workflows and ML services using observability tools that track latency failure rates and resource usage to proactively resolve incidents and improve stability.
- Optimize pipeline performance by tuning task dependencies parallelism and resource allocation so that data processing and model inference meet agreed service levels.
- Document architecture decisions pipeline designs and operational runbooks in clear and accessible formats that enable knowledge sharing and reduce onboarding time for new team members.
- Coordinate with security and compliance stakeholders to embed access control audit readiness and data protection practices into Airflow and ML Ops implementations.
- Provide technical guidance to peers on Airflow patterns and ML Ops best practices that encourage consistent coding standards and robust engineering habits across the team.
- Engage with product and business partners to understand analytical needs and convert them into actionable pipeline backlogs that align with organizational goals and customer impact.
- Participate in code reviews design discussions and continuous improvement initiatives that elevate code quality reduce defects and enhance maintainability of the overall platform.
Qualifications
- Demonstrate strong hands on proficiency in building and scheduling Airflow directed acyclic graphs that handle dependency management and error recovery for complex data pipelines.
- Exhibit proven experience in ML Ops including model packaging deployment automation monitoring and retraining cycles using modern tooling and cloud native practices.
- Show advanced capability in Python programming for data processing pipeline orchestration and integration with machine learning frameworks within production systems.
- Apply solid understanding of data engineering concepts such as batch and streaming processing data warehousing and structured logging to support reliable model operations.
- Display familiarity with containerization and orchestration technologies that enable scalable deployment of ML services and Airflow workers in hybrid environments.
- Possess experience working in agile delivery models with emphasis on iterative development backlog refinement and close collaboration with multidisciplinary teams.
- Bring strong problem solving skills and the ability to diagnose pipeline failures performance bottlenecks and model drift by using metrics and logs to drive corrective actions.
- Communicate clearly with technical and non technical partners by explaining pipeline behavior risks and remediation plans in a concise and outcome oriented manner.
- Prefer exposure to cloud platforms and managed ML services that enhance reliability scalability and security of deployed machine learning solutions.
- Cherish a mindset of continuous learning that keeps skills current in emerging ML Ops practices Airflow enhancements and data platform innovations.
Salary and Other Compensation:
Applications will be accepted until Month Day, Year.
The annual salary for this position is between $70-80K depending on experience and other qualifications of the successful candidate.
This position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans.
Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:
· Medical/Dental/Vision/Life Insurance
· Paid holidays plus Paid Time Off
· 401(k) plan and contributions
· Long-term/Short-term Disability
· Paid Parental Leave
· Employee Stock Purchase Plan
Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable la
Über Cognizant
Cognizant (NASDAQ: CTSH) i ist ein Technologiedienstleister und Entwickler von KI-Lösungen. Wir schlagen die Brücke zwischen KI-Investitionen und echtem unternehmerischem Mehrwert, indem wir ganzheitliche Full-Stack-KI-Lösungen für unsere Kunden entwickeln. Mit unserer fundierten Branchen-, Prozess- und Engineering-Expertise integrieren wir die spezifischen Anforderungen von Unternehmen passgenau in Technologiesysteme. So entfalten wir das menschliche Potenzial, erzielen greifbare Ergebnisse und sichern globalen Unternehmen in einer sich rasant wandelnden Welt den entscheidenden Vorsprung. Erfahren Sie mehr unter cognizant.ai oder @cognizant.
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