AI Engineer

• Design, develop, and maintain robust, scalable, and high-performance object oriented code in our backend services.
• Develop public REST APIs using Java and internal gRPC APIs for inter-service and inter-system communication.
• Craft systems designs, lead design decisions, and drive alignment with other senior engineers.
• Write automated unit tests, integration tests, end-to-end tests, concurrency tests, load/performance tests.
• Analyze existing systems to identify bottlenecks, tech debt, and implement scalability, and stability improvements.
• Implement automation for testing, monitoring, healing, and scaling applications, continuous integration and deployment to reduce time to market.
• Collaborate with cross-functional teams, including product managers, designers, and other engineers, to define and implement new features.
• Conduct code reviews (comment, approve, seek revisions, merge), mentor junior and mid-level engineers, and actively promote engineering best practices.
• Dive deep and troubleshoot complex issues, devise fixes, author root cause analysis documents, and ensure lasting performance and reliability.
• Conduct objective and comparative analyses of competing technologies to advise the team of pros and cons of a technology solution.
• Maintain robust documentation (design docs, run books, change management docs, and readiness plans).
• Provide live-site support for production applications by monitoring systems, ensuring rapid incident resolution, and driving continuous improvement.
• Drive cross-team projects as a single-threaded-owner (STO) or tech lead, and actively unblock other engineers to make progress.
• Design and build agentic AI systems and services, enabling autonomous workflows, reasoning, and task execution within Mobility platforms.
• Develop AI agents from scratch, including orchestration, tool usage, memory, and multi-step decision-making capabilities.
• Implement and scale multi-agent architectures to support complex, distributed use cases across payments and fleet ecosystems.
• Integrate systems using Model Context Protocol (MCP) or similar frameworks to enable secure and scalable interaction between AI agents, APIs, and enterprise data sources.
• Build and optimize LLM-powered services (e.g., OpenAI APIs, LangChain) for production-grade performance, reliability, and cost efficiency.

Software Engineer 2 – AI Platform

• Turn quantitative prototypes into reusable tools, services, packaged desktop applications, interfaces, and workflow products that can move from internal demo to client pilot to scaled offer.
• Ship across both cloud-hosted services and locally distributed desktop applications, including Electron-based apps when the workflow or client environment calls for it.
• Build enterprise hardening into the productization layer, including authentication, role-based access control (RBAC), observability, security, release quality, cost controls, and deployment discipline.
• Build evaluation, regression, and release discipline into the productization layer so model logic and agent behavior remain measurable as systems change.
• Work closely with the quant lead so model logic, evaluation intent, and governance requirements survive the move into production.
• Make pragmatic architecture choices across large language models (LLMs), deterministic rules, and hybrid systems based on value, latency, cost, and reliability.
• Help shape repeatable build patterns so strong prototypes become faster, more reliable, and more reusable over time.

QuantAI Engineer – Trading, Investment & Optimization

• Define and drive the vision, strategy, and roadmap for enterprise semantic data and data connectivity across business domains
• Partner with business and IT leaders to identify high-value opportunities to connect and standardize data across systems
• Position the semantic data product as a product, not just a technical asset, with clear customers, outcomes, adoption goals, and success metrics
• Define and evolve the product’s value proposition, ensuring alignment to business priorities and measurable outcomes
• Act as the general manager of the product, balancing user needs, technical feasibility, strategic priorities, operational constraints, and business impact
• Define and manage data products aligned to key business domains, including clear ownership, users, and success metrics
• Define target users and use cases for data products, ensuring alignment to specific decisions, workflows, and business outcomes
• Partner closely with business customers to ensure data products support critical decisions, workflows, and operational outcomes
• Drive adoption and usage of shared data assets across business units
• Identify and prioritize use cases where connected data unlocks measurable business value (e.g., improved decisioning, automation, personalization)
• Ensure solutions are aligned to real-world workflows and deliver tangible outcomes
• Lead the development and evolution of enterprise ontologies, taxonomies, and data models that represent key business concepts and relationships
• Establish a scalable semantic layer that enables reuse across analytics, AI, and operational use cases
• Ensure alignment of definitions across domains to reduce fragmentation and duplication
• Support both structured and unstructured data integration, enabling downstream AI and analytics applications
• Ensure data is discoverable, understandable, and usable across teams through consistent definitions and relationships
• Partner deeply with business domains to understand workflows, decision points, and data dependencies, acting as a product owner for how data supports those functions
• Own the end-to-end product lifecycle from discovery and definition to delivery and iteration
• Define product requirements, success metrics, and release plans
• Continuously refine the semantic data product based on user feedback and evolving business needs
• Define critical metrics to measure adoption, usability, and impact of the semantic data product
• Supervise usage and continuously improve accessibility and value delivery
• Evangelize the value of connected semantic data across the organization
• Partner with data governance teams to establish standards, definitions, and data quality expectations
• Promote consistency, reusability, and scalability of data assets across the enterprise

Senior Manager, AI Lead – Semantic Layer

• Architect and lead the development of agentic AI systems that automate and augment finance workflows.
• Design and implement multi-agent systems leveraging LLMs, tool-use frameworks, and orchestration patterns.
• Translate cutting-edge research in LLMs and agentic AI into scalable, production-ready solutions.
• Establish guardrails, evaluation frameworks, and responsible AI practices to ensure safe, compliant, and reliable outputs.
• Lead the design and implementation of advanced predictive models, including time series forecasting and attrition prediction across customer segments.
• Develop interpretable, production-grade models that drive retention strategies and financial planning.
• Define and standardize evaluation metrics, validation frameworks, and monitoring systems for model performance and drift detection.
• Design and build scalable AI/ML systems with a strong emphasis on software engineering best practices.
• Develop and integrate AI services into internal applications and workflows.

Principal Applied AI Engineer, Finance

• Design and implement scalable APIs for agent orchestration and learner interaction
• Build data pipelines that feed agent evaluation and continuous improvement cycles
• Create event streaming infrastructure for real-time learner interaction analysis
• Ensure data quality and accessibility for training and evaluating learning agents
• Improve data infrastructure for cost and performance in close coordination with Data and ML teams
• Design and implement rigorous evaluation frameworks to measure agent performance and improve cycles
• Develop LLM evaluation processes and perform error analysis to identify systemic improvements for agentic learning systems
• Support instrumentation that makes evaluation relevant (metrics, traces/logs, and analysis workflows)
• Participate in on-call rotation and incident response for learning platform reliability
• Contribute to runbooks, postmortems, and reliability improvements as we evolve our operating model

Senior Software AI Engineer

• Design, develop, and maintain robust, scalable, and high-performance object oriented code in our backend services.
• Develop public REST APIs using Java and internal gRPC APIs for inter-service and inter-system communication.
• Craft systems designs, lead design decisions, and drive alignment with other senior engineers.
• Write automated unit tests, integration tests, end-to-end tests, concurrency tests, load/performance tests.
• Analyze existing systems to identify bottlenecks, tech debt, and implement scalability, and stability improvements.
• Implement automation for testing, monitoring, healing, and scaling applications, continuous integration and deployment to reduce time to market.
• Collaborate with cross-functional teams, including product managers, designers, and other engineers, to define and implement new features.
• Conduct code reviews (comment, approve, seek revisions, merge), mentor junior and mid-level engineers, and actively promote engineering best practices.
• Dive deep and troubleshoot complex issues, devise fixes, author root cause analysis documents, and ensure lasting performance and reliability.
• Conduct objective and comparative analyses of competing technologies to advise the team of pros and cons of a technology solution.
• Maintain robust documentation (design docs, run books, change management docs, and readiness plans).
• Provide live-site support for production applications by monitoring systems, ensuring rapid incident resolution, and driving continuous improvement.
• Drive cross-team projects as a single-threaded-owner (STO) or tech lead, and actively unblock other engineers to make progress.
• Design and build agentic AI systems and services, enabling autonomous workflows, reasoning, and task execution within Mobility platforms.
• Develop AI agents from scratch, including orchestration, tool usage, memory, and multi-step decision-making capabilities.
• Implement and scale multi-agent architectures to support complex, distributed use cases across payments and fleet ecosystems.
• Integrate systems using Model Context Protocol (MCP) or similar frameworks to enable secure and scalable interaction between AI agents, APIs, and enterprise data sources.
• Build and optimize LLM-powered services (e.g., OpenAI APIs, LangChain) for production-grade performance, reliability, and cost efficiency.
• Implement evaluation frameworks, observability, and guardrails to ensure correctness, safety, and compliance of AI-driven systems.
• Design solutions for context management, memory, and retrieval-augmented generation (RAG) to enhance agent effectiveness.

• Develop comprehensive systems and frameworks for AI applications and products.
• Construct prototypes and minimum viable products to validate AI/ML solutions before committing substantial resources.
• Develop, refine, and productionize AI systems using cloud resources such as Azure OpenAI, Azure ML, and Cognitive Services.
• Contribute to the creation of robust data pipelines for model evolution and production systems.
• Develop specifications for low latency APIs and services necessary to deploy AI models and incorporate them into applications.
• Supervise and sustain AI systems in production to guarantee consistent model accuracy and reliability.
• Actively participate in a team that exercises principled, agile-like development practices.
• Create and maintain thorough documentation that is consistent with team procedures, corporate policies, and expectations.
• Ensure peer review on all assigned work, as well as conduct peer reviews over the work of others as requested.
• Guide junior AI engineers on AI/ML and industry best practices and methodologies.
• Keep abreast of new tools and concepts through reading documentation or literature and actively practicing skills development.
• Support and participate in meetings with external stakeholders.

AI Engineer – III

• Design, develop, and deploy scalable AI solutions
• Build LLM-based solutions for enterprise applications
• Integrate LLMs with business systems
• Architect AI agents for intelligent automation
• Implement CI/CD pipelines for machine learning models
• Monitor model performance and governance
• Collaborate with cross-functional teams for solution delivery
• Create technical documentation and mentoring

• Play a key role in building the internal platform which supports training and deploying AI models across the entire organization.
• Take ownership of defining the platform to enable AI model fine-tuning and batch inference by building the SDKs and supporting the infra.
• Manage cross-cutting stakeholder relationships, prioritizing customer needs first.
• Navigate ambiguous, hairy, and technical problem spaces.
• Jump into domains, languages, and problem areas that might be new and unfamiliar.

Senior Software Engineer II, ML/AI Platform

• Architect and lead the development of agentic AI systems that automate and augment finance workflows (e.g., forecasting, reporting, and decision support).
• Design and implement multi-agent systems leveraging LLMs, tool-use frameworks, and orchestration patterns (e.g., RAG, model chaining, dynamic prompting).
• Translate cutting-edge research in LLMs and agentic AI into scalable, production-ready solutions.
• Establish guardrails, evaluation frameworks, and responsible AI practices to ensure safe, compliant, and reliable outputs.
• Design fault-tolerant, observable agent systems with clear failure modes and recovery strategies.
• Lead the design and implementation of advanced predictive models, including time series forecasting and attrition prediction across customer segments.
• Develop interpretable, production-grade models that drive retention strategies and financial planning.
• Define and standardize evaluation metrics, validation frameworks, and monitoring systems for model performance and drift detection.
• Translate complex predictive insights into actionable recommendations for finance and business leaders.
• Design and build scalable AI/ML systems with a strong emphasis on software engineering best practices (modular design, APIs, CI/CD, testing).
• Lead end-to-end development from concept to production, ensuring robustness, scalability, and maintainability.
• Develop and integrate AI services into internal applications and workflows, including light front-end/back-end components where needed.
• Drive adoption of modern tooling (e.g., containerization, orchestration, cloud-native architectures).
• Establish and enforce MLOps best practices for deployment, monitoring, retraining, and governance of AI systems.
• Ensure systems meet enterprise standards for security, compliance (e.g., SOX), and auditability.
• Develop advanced feature engineering strategies capturing behavioral, financial, and temporal signals.
• Set technical direction for AI/ML initiatives across the finance organization.
• Lead complex, cross-functional projects and mentor other data specialists.
• Work alongside stakeholders across finance, IT, and product to adopt AI-driven solutions.
• Contribute to long-term AI strategy, identifying opportunities to drive efficiency and innovation.

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Software Engineer 2 – AI Platform

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Principal Applied AI Engineer, Finance