Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process services, dedicated to helping the world's leading companies build stronger businesses. Headquartered in Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industry and business process expertise, and a global, collaborative workforce that embodies the future of work. Cognizant is a member of the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world.
Our Culture
Your passion, integrity and experience are integral to Cognizant's success. You will join a dynamic and expanding global leader in IT and Business consultancy where you will be valued for who you are. We take pride in our partnership with our clients, so your ability to add value and provide exceptional service to our clients is fundamental to your success. In return, you will be presented with opportunities to develop your career and collaborate with talented colleagues in a supportive, diverse environment.
At Cognizant we recognize that companies that are open and welcoming to a multi-culturally diverse workforce will thrive with fresh perspectives and collaborative knowledge. Cognizant focuses on promoting & increasing gender diversity and providing a workplace which encourages great participation and an equal playing field, where merit and accomplishment are the only criteria for success.
Job Summary:
We are seeking an ML Engineer to build, deploy, and operationalize machine learning models as part of a data platform modernization program for an Australian payments company. This role focuses on end-to-end ML pipeline development from feature engineering and model training to deployment, monitoring, and inference using AWS SageMaker and related services. The ideal candidate can translate Data Scientist prototypes into production-grade ML systems with robust monitoring and automation.
Key Responsibilities:
· Develop end-to-end ML pipelines covering feature engineering, model training, evaluation, deployment, and monitoring on AWS SageMaker.
· Build and maintain feature pipelines that feed a centralized feature store for use across multiple ML models (e.g., churn, propensity, segmentation).
· Deploy ML models as real-time (endpoint) and batch (scheduled) inference services with SageMaker.
· Implement model monitoring, performance tracking, and data/model drift detection mechanisms.
· Collaborate with Data Scientists to productionize experimental models and optimize for latency, cost, and reliability.
· Build ML pipeline orchestration using SageMaker Pipelines, Step Functions, or equivalent workflow tools.
· Integrate model outputs with downstream systems (e.g., Snowflake, Salesforce, BI tools) via reverse ETL or API patterns.
· Implement and manage model versioning, A/B testing, and champion/challenger deployment patterns.
· Contribute to the MLOps framework, including CI/CD for ML, automated retraining triggers, and experiment tracking (e.g., MLflow).
Required Skills & Experience:
· Deep hands-on experience with SageMaker for training, hosting, pipelines, and model registry.
· Expert-level Python for ML development (scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow).
· Hands-on experience deploying ML models to production endpoints (real-time and batch).
· Experience implementing drift detection, performance dashboards, and automated alerting for deployed models.
· Understanding of MLOps principles: CI/CD for ML, experiment tracking, model versioning, and automated retraining.
· Strong SQL skills for feature extraction and data validation from Snowflake or similar warehouses.
Preferred Skills (Nice-to-Have):
· Experience with MLflow, Weights & Biases, or equivalent experiment tracking tools.
· Familiarity with Snowflake Cortex or Snowpark ML.
· Knowledge of LLM integration patterns and AWS Bedrock.
· Experience building propensity, churn, or recommendation models in fintech or payments.
· Familiarity with Docker, Kubernetes, or containerized ML deployments.
· AWS Certified Machine Learning – Specialty certification.
Benefits:
Joining Cognizant will give you the opportunity to learn and collaborate with some of the most talented people in the industry, while having your finger on the pulse of emerging industry trends and working on the cutting edge of technology in your field of expertise.
We recognize that our people perform at their best when they feel valued as significant contributors and that is why at Cognizant, taking care of our employees is a priority:
· You can pursue innovative career tracks and opportunities here
· You can enhance your professional development through education and dedicated training
· We’ll give you the skills you need to keep pace with the changing workplace while our compensation, benefits and wellness packages help you stay healthy and plan for the future.
Next Steps:
Please reach out to our friendly and welcoming team today to apply and register your interest for this full-time role. We'd love to help you get your next role and enable you to fulfil your professional ambitions.
コグニザントについて
コグニザント（NASDAQ: CTSH）は、AI Builderおよびテクノロジーサービスプロバイダーとして、お客様にフルスタックのAIソリューションを構築することで、AI投資と企業価値を結ぶ架け橋となっています。業界、ビジネスプロセス、エンジニアリングに関する当社の深い専門知識を活かし、組織固有のビジネス環境をテクノロジー・システムに組み込みます。これにより、人間の可能性を最大限に引き出し、確かな成果を実現するとともに、急速に変化する世界においてグローバル企業が常に一歩先を行くための支援を行っています。 詳細については、cognizant.ai をご覧ください。
雇用に関する追加情報
本募集に記載されている報酬情報は、掲載日時点で正確なものです。Cognizantは、適用される法令に従い、いつでも本情報を変更する権利を留保します。
応募者は、対面またはビデオ会議による面接への参加を求められる場合があります。また、各面接の際に、現在有効な州政府または政府発行の身分証明書の提示を求められる場合があります。
Cognizantは機会均等雇用主です。応募および選考において、人種、肌の色、性別、宗教、信条、性的指向、性自認、国籍、障がい、遺伝情報、妊娠、退役軍人の地位、その他連邦法・州法・地方自治体の法律により保護されるいかなる特性に基づく差別も行いません。
