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Design and optimize high-throughput batch and streaming data pipelines, cloud warehouses, and lakehouse architectures for financial and transactional data. Implement data quality, testing, monitoring, DataOps, CI/CD, infrastructure as code, governance, privacy, and security practices. Collaborate with cross-functional teams, ensure reliability and compliance, and mentor junior and mid-level data engineers.

Design, build, and operate shared AI foundations, LLM gateways, model-routing systems, agentic workflows, evaluation frameworks, guardrails, and observability pipelines. Deliver production-grade AI capabilities end to end, establish engineering standards, and develop automation and developer tools that improve velocity. Balance reliability, security, privacy, scalability, latency, quality, and cost while collaborating with international technical stakeholders.

The job involves leading engineering teams in developing scalable software solutions, overseeing architecture, enforcing standards, and ensuring operational excellence while managing client relationships at senior levels.

The Senior Engineering Manager leads the technical execution of digital products, overseeing engineering governance, operational excellence, and strategic client partnerships while managing teams and ensuring high-quality delivery.

Serve as the bridge between payment business stakeholders and technical teams for a banking payments modernization program. Gather and document requirements, create epics and user stories, map payment workflows, identify automation opportunities, and support Agile delivery in Jira. Collaborate on APIs, integrations, testing, defect resolution, and production readiness. Ensure solutions address regulatory controls, auditability, segregation of duties, BSA/AML, OFAC, fraud prevention, and security requirements across Wire, RTP, FedNow, ACH, and digital payment channels.

Build and operate scalable DataOps solutions across data engineering, cloud infrastructure, and DevOps. Develop Python, Bash, Terraform, and pipeline automation; orchestrate systems through APIs; deploy and maintain AWS infrastructure; support data modeling and documentation; establish DataOps best practices; and collaborate directly with customers to gather requirements and propose technical solutions.

Leads end-to-end data science initiatives, including statistical modeling, machine learning, feature engineering, deployment, monitoring, and optimization. Designs scalable AWS-based solutions, guides technical decisions, mentors data professionals, collaborates with business and engineering stakeholders, and develops data strategies and best practices. Requires strong Python, SQL, machine learning, cloud, communication, and technical leadership skills.

Design, build, and optimize high-throughput batch and streaming data pipelines, cloud data warehouses, and lakehouse architectures for financial and transactional data. Implement data quality, testing, monitoring, DataOps, CI/CD, infrastructure as code, governance, privacy, and security practices. Collaborate with technical and product teams, ensure reliable and compliant production systems, and mentor junior and mid-level data engineers.

Own the architecture, development, automation, and production deployment of end-to-end machine learning solutions. Manage the ML lifecycle, evaluate cloud architectures for performance and cost efficiency, integrate with data engineering and software teams, troubleshoot complex systems, align solutions with business needs, and mentor teams on MLOps adoption.

Lead modernization of legacy enterprise data systems: refactor backend database logic into Python, build and optimize APIs and backend with Node.js/Python, deliver ReactJS + Material-UI dashboards, tune complex T-SQL/SQL Server queries and stored procedures, manage deployments with Kubernetes and CI/CD, and collaborate with global teams on testing and documentation.