10 hours ago
Base Salary
$155k - $170k/yr
Responsibilities
- Design and implement end-to-end MLOps architectures using Snowpark, Snowflake ML, Model Registry, Feature Store, Container Runtime, and Snowpark Container Services.
- Define batch, real-time, and near-real-time inference architectures, including compute optimization, GPU allocation, warehouse sizing, and cost management.
- Build CI/CD and continuous-training workflows using GitHub Actions, Azure DevOps, GitLab, Jenkins, Snowflake CLI, and Git Integration.
- Design orchestration solutions with Snowflake Tasks, Task Graphs, Dynamic Tables, Streams, dbt, and Apache Airflow.
- Deploy and monitor machine learning models, including drift detection, performance monitoring, A/B testing, champion/challenger strategies, and rollback processes.
- Establish governance, security, compliance, model risk management, auditability, and secure AI operations practices.
- Serve as the technical authority for MLOps architecture, mentor technical teams, and develop reusable frameworks, standards, and implementation accelerators.
Requirements
- Bachelor’s degree in Computer Science, Data Science, Engineering, Information Technology, or a related discipline, or equivalent work experience.
- At least 8 years of experience in data engineering, machine learning engineering, data platforms, or cloud architecture.
- At least 3 years of experience designing and implementing MLOps and ML platform architectures.
- Strong hands-on experience with Snowflake, Snowpark Python, stored procedures, UDFs, UDTFs, Tasks, Streams, Dynamic Tables, Snowflake ML APIs, Model Registry, and Feature Store.
- Advanced expertise in Python, SQL, machine learning frameworks, and MLOps practices, with experience using Scikit-Learn, XGBoost, LightGBM, PyTorch, and TensorFlow.
- Experience implementing CI/CD pipelines, infrastructure as code, and Docker-based containerization.
- Knowledge of ML monitoring, drift detection, model governance, and responsible AI practices.
- Experience with at least one major cloud platform: AWS, Azure, or GCP.
- Preferred qualifications include SnowPro certifications, Snowpark Container Services, GPU workloads, distributed training, Ray, hyperparameter optimization, Cortex Functions, Cortex Search, Cortex Agents, RAG, LLM fine-tuning, MLflow, dbt, Apache Airflow, Kafka, OpenFlow, and ML workload migration experience.
- Healthcare, insurance, or financial services experience is preferred.
Benefits
- Hybrid work model based in Boston, Massachusetts, with onsite and remote work determined by business and client requirements; occasional travel may be required.
- Medical, dental, and vision insurance, HSA and FSA, life insurance, AD&D, short- and long-term disability coverage, PTO, and company holidays.
- 401(k) plan with company contributions, employee stock purchase plan where available, employee assistance program, parental leave, and family support benefits.
- Tuition reimbursement, professional development, learning, certification, career development, and leadership development opportunities.
Tech Stack
Apache AirflowApache KafkaAWSAzureDatabricksdbtDockerGitHub ActionsGoogle Cloud PlatformJenkinsLightGBMMLflowPythonPyTorchscikit-learnSnowflakeSQLTensorFlowXGBoost
Categories
About Cognizant
Cognizant is a public IT services and consulting firm that designs, builds, and runs enterprise technology, including digital engineering, cloud modernization, data/AI, and managed services. It sells consulting, systems integration, and outsourcing on multi-year engagements to large enterprises in healthcare, banking, retail, communications, and manufacturing. Founded in 1994 and headquartered in Teaneck, New Jersey, Cognizant is NASDAQ-listed (CTSH) and a Fortune 500 company.