A client-focused role to support the implementation, configuration, and optimization of AI-powered compliance and financial crime solutions on a FinTech platform.
Responsibilities
Support client onboarding onto the FinTech platform by contributing to requirements gathering and configuration tasks.
Provide effort estimates for your assigned tasks and keep your TPM and team lead informed of progress and blockers.
Participate in client and internal workshops, capture notes and actions, and help follow through on implementation activities.
Turn functional and regulatory requirements into configuration specifications, working from detailed specifications and reviewed designs.
Work with Solution Architects, senior consultants, and Engineering to implement solutions; escalate design decisions to senior colleagues.
The Data Engineer designs, secures, and maintains data pipelines on cloud environments (Azure, AWS, Databricks), collaborating with business and technical teams.
Responsibilities
Design, secures, and maintains data pipelines on cloud environments (Azure, AWS, Databricks)
Design and build scalable end-to-end data pipelines (ETL/ELT) ingesting heterogeneous data sources into curated, analytics-ready formats
Implementation of DataOps and DevOps best practices: automated CI/CD pipelines, automated testing, version control (Git), and deployment automation
Leverage agentic acceleration and AI-assisted tooling to boost engineering productivity
Ensure maintenance in operational conditions (MCO), security compliance, resilience, and horizontal scalability of managed data platforms and pipelines
Requirements
Nice to have: JIRA
Nice to have: Confluence
Nice to have: Cloud certifications (Azure, AWS, Databricks)
Lead technical onboarding and solution design for Argos99 customers, serving as the primary technical point of contact and ensuring successful implementation and ongoing support.
Responsibilities
Serve as the named technical point of contact for a portfolio of Argos99 customers, from contract signature through renewal.
Lead technical onboarding: kickoff, onboarding plan, connectivity and access, and sign-off against agreed success criteria.
Configure and validate data sources across identity, endpoint, infrastructure, network and backup systems, and verify data quality in the Argos99 knowledge graph.
Design solutions for customer needs the platform doesn't yet cover, including new collectors and integrations such as ServiceNow CMDB, and write clear requirements for engineering.
Coordinate customer-side testing and validate delivered solutions with the customer before go-live.
Requirements
Bachelor's degree in a technical field or equivalent practical experience
Rivian is seeking an innovative engineer who combines expertise in AI/ML with a passion for quality and manufacturing excellence in the Body Assembly area. This role is crucial in driving our digital transformation initiatives through the development and implementation of AI-powered solutions.
Compensation
The salary range for this role is $98,500-$123,100 for Illinois based applicants.
Responsibilities
Apply fundamental process engineering principles and advanced statistical methods, including ML/AI algorithms, to analyze manufacturing data (e.g., End of line test data, sensor data, press data, LVDT data, historical trends, operational logs).
Develop and implement predictive models for key performance indicators (KPIs) such as yield, throughput and equipment downtime.
Collaborate with IT and operations teams to establish data pipelines, ensuring data quality, accessibility, and integrity for ML model training and deployment.
Utilize data collected by Rivian’s MES database system for model training.
Design, train, validate, and deploy robust ML models (e.g., classification, regression, time-series forecasting) for real-time process control, anomaly detection, predictive maintenance, and quality prediction.