AI Engineer

• Design, develop, and deploy machine learning models and AI-powered applications
• Collaborate with cross-functional teams to identify and implement AI/ML solutions for client needs
• Build and maintain data pipelines, model training workflows, and inference systems
• Evaluate and integrate large language models (LLMs), generative AI tools, and emerging AI frameworks
• Monitor model performance, conduct testing, and iterate on improvements
• Document technical architectures, methodologies, and best practices
• Stay current with AI/ML research and translate advancements into practical applications

• Define and evolve enterprise AI architecture, standards, reference models and implementation guidelines to support consistent solution delivery across business functions
• Align AI initiatives to enterprise strategies, roadmaps, and business priorities
• Identify and prioritize AI opportunities that drive student success and operational efficiency
• Design scalable AI platforms and solutions across data, application, and technology domains
• Provide guidance on how AI, ML, and automation will improve current and future-state architectures
• Lead design and implementation of AI-powered solutions (e.g., LLMs, agents, automation, and analytics)
• Integrate AI capabilities with enterprise applications and data platforms
• Guide evaluation and selection of AI/ML models, frameworks, and vendors
• Establish reusable patterns, APIs, and integration approaches
• Establish governance practices covering model oversight, responsible AI usage, security controls, compliance expectations, and lifecycle management
• Establish AI governance, including responsible AI, security, privacy, and compliance (FERPA, HIPAA, GDPR)
• Establish lifecycle management practices (MLOps/LLMOps, model monitoring, drift detection, retraining)
• Implement performance, bias, and risk monitoring frameworks
• Ensure secure AI architecture (e.g., data protection, isolation, adversarial defense)
• Partner with business, product, data, and engineering teams to provide AI solutions
• Provide architectural guidance to architects and delivery teams
• Facilitate workshops, POCs, demos and AI adoption projects identifying viable opportunities where AI can be a positive differentiator
• Mentor teams on AI best practices, patterns, and frameworks

Enterprise AI Architect

• Architect and lead the development of agentic AI systems that automate finance workflows.
• Design and implement multi-agent systems leveraging LLMs and orchestration patterns.
• Translate cutting-edge research into scalable solutions.
• Establish guardrails and responsible AI practices for safe outputs.
• Lead the design of advanced predictive models for customer behavior forecasting.
• Develop interpretable, production-grade models for retention strategies.
• Design scalable AI/ML systems with emphasis on software engineering best practices.
• Establish MLOps best practices for AI systems deployment and governance.
• Set technical direction for AI/ML initiatives across finance.

Principal Applied AI Engineer, Finance

• Design, implement, and maintain cloud-native infrastructure for AI applications on AWS and Azure
• Build and manage CI/CD and GitOps workflows
• Develop production-grade services and platform components in Python
• Implement and maintain Infrastructure-as-Code (IaC)
• Collaborate with AI/ML engineers to integrate LLMs into products
• Operate as a system administrator-level engineer for cloud services
• Champion AI-native development practices
• Contribute to platform standards for security and compliance
• Participate in on-call rotations and incident response

Senior Software Engineer – AI Platform

• 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.

AI Engineer jobs in Maine

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AI Engineer

Enterprise AI Architect

Principal Applied AI Engineer, Finance

Senior Software Engineer – AI Platform

Software Engineer 2 – AI Platform

QuantAI Engineer – Trading, Investment & Optimization