AI Engineer

• Design, build, test, and deploy AI legal workflows and skills across firm-approved AI tools, supporting use cases such as contract review, due diligence, drafting, research, and summarization
• Partner directly with attorneys and knowledge management professionals to identify high-value use cases, map workflows, and translate practice needs into structured AI solutions
• Develop and maintain retrieval pipelines, document templates, and reusable workflow components with clear, attorney-ready documentation
• Build and execute evaluation frameworks to measure workflow quality, accuracy, and reliability; monitor performance and iterate based on feedback and outcomes
• Serve as a go-to resource for enablement and change management for developed workflows, partnering with the AI Adoption team to drive usage, deliver training sessions and workshops, 1:1 coaching, and develop practical playbooks and materials that support sustained adoption
• Develop and scale best-practice standards, use-case libraries, and playbooks that empower attorneys to responsibly and independently leverage AI tools while maintaining quality and risk controls
• Embed human-review checkpoints and audit mechanisms aligned with Firm governance, confidentiality, and professional responsibility requirements
• Collaborate closely with cross-functional stakeholders including the AI Product team, AI Adoption team, Knowledge & Innovation team, IT, Innovation Practice Group, and Professional Development to ensure aligned delivery and support
• Track adoption, usage, and impact metrics for deployed workflows and recommend refinements or expansion opportunities
• Manage ongoing maintenance of production workflows, including updates to prompts, retrieval sources, and guardrails in response to model and platform changes, user feedback, or evolving practice needs
• Represent the AI Products & Adoption team in internal meetings, committees, and practice group sessions

AI Legal Engineer

• Build our agentic SDLC pipeline. Design, implement, and operate agentic workflows across the development lifecycle - planning, code generation, review, testing, documentation, and release that is all integrated with our GitOps and CI/CD infrastructure.
• Level up developer experience. Bring modern AI integrations into the daily workflow of our engineering team: coding agents, automated review, intelligent test generation, and self-service tooling that removes friction and multiplies output.
• Engineer grounded, trustworthy systems. Build retrieval-grounded and context-aware AI systems that produce reliable, verifiable results and design the evaluation harnesses that prove it.
• Establish governance and guardrails. Define and implement the policies, human-in-the-loop checkpoints, audit trails, and security controls that let us adopt agentic AI responsibly in environments where trust is non-negotiable.
• Formalize and scale. Turn what works into repeatable frameworks, playbooks, and internal platforms that extend agentic acceleration beyond engineering to the whole organization.
• Champion the practice. Evangelize, teach, and support AI adoption across the team; you'll be a force multiplier, not a silo.

AI Workflow Engineer

• Ship and debug code on a live, real-time voice pipeline
• Design control systems around LLMs
• Build and operate LLM evaluation and batch-analysis pipelines
• Own traditional ML workflows from data to scheduled production inference
• Trace production issues from a metric anomaly to root cause

Senior Software Engineer – Applied AI

• Develop and maintain relationships with homebuyers, Realtors, builders, financial advisors, and referral partners.
• Guide borrowers through the mortgage process from application through closing.
• Analyze financial information and recommend appropriate loan solutions.
• Build and manage a consistent referral pipeline through networking and relationship development.
• Deliver exceptional customer service throughout the lending process.
• Maintain compliance with all federal, state, and company lending guidelines.
• Grow a sustainable book of business through outstanding client experiences and referral relationships.

Licensed Mortgage Loan Originator

• Own stakeholder relationships across Medbridge’s executive team, working with them to find the problems and AI enabled solutions that can expedite, or remove, the current blockers in the business.
• Design, build, and ship AI-powered features across our internal teams, from the user interface to the backend services and AI workflows behind it.
• Build full-stack applications using AI-native engineering workflows and tools such as Claude, Cursor, Codex, and OpenAI and Anthropic APIs.
• Develop APIs, backend services, and automation pipelines that support intelligent product capabilities and production-ready deployments.
• Apply software engineering, DevOps, and MLOps best practices to the testing, deployment, monitoring, evaluation, and optimization of AI solutions.
• Partner closely with our Product and Engineering teams to turn ambiguous goals into scalable internal products.
• Use AI tooling throughout your workflow (coding, code review, testing, and planning) and help push the team’s adoption forward.
• Research emerging AI technologies, LLM implementation patterns, and modern product engineering frameworks, and bring what works back to the team.
• Instrument what you ship and use usage data and evidence to decide what to build, fix, or kill next.

Product Engineer – AI

Machine Learning Engineer

• Build statistical models and production systems to balance advertiser performance with business goals.
• Tune optimization parameters, measure internal competition, and model dynamic environments.
• Design and run experiments to validate theories underpinning the mobile ad tech economy.
• Develop applications in the areas of advertiser budget retention and growth, optimal margin allocation, and bidding innovations.
• Collaborate with a team of world-class engineers with diverse backgrounds as well as peers across the broader company (e.g. Operations, GTM).
• Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences.
• Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and design/code review.

• Accountable for executing and driving mid-size feature application design and development efforts to completion, serving as a development lead on medium-scale projects and supporting the execution of larger efforts.
• Proficient at operating with considerable autonomy and discretion as you will significantly influence the way an application is designed and developed by providing subject specific expertise and an advanced level of understanding of application programming principles.
• Manage one or more Applications Development teams in an effort to accomplish established goals as well as conduct personnel duties for team (e.g. performance evaluations, hiring and disciplinary actions).
• Utilize in-depth knowledge and skills across multiple Applications Development areas to provide technical oversight across systems and applications.
• Lead the design, development, and implementation of sophisticated AI/ML, Generative AI, and Agentic AI solutions, including AI assistant tools, in close collaboration with AI architects, product owners, and cross-functional engineering teams within the Engineering Excellence framework.
• Architect and develop end-to-end AI systems, leveraging both Python for machine learning workflows and Java for scalable enterprise-grade backend services, ensuring seamless integration with existing and new applications.

AI/ML Engineer – Full Stack Developer

• Own technical direction of the ML Platform — feature store, model hosting and serving, experimentation, training infrastructure — driving architectural decisions around scalability, reliability, latency, and cost
• Lead design and delivery of large-scope platform initiatives from conception through production, coordinating across ML, data, and infrastructure teams
• Identify and resolve systemic technical challenges: online/offline feature parity, model deployment friction, experimentation velocity, GPU utilization, cross-team dependencies
• Set and maintain a high engineering quality bar through hands-on code contributions, design reviews, and mentorship of platform and ML-adjacent engineers
• Partner with ML engineering, data science, product, and platform leadership to translate ML strategy into technical roadmaps
• Define the paved paths ML teams use to ship models safely — from feature registration through canary rollout, monitoring, and rollback
• Leverage AI-augmented development tools to increase development velocity and code quality

Staff Machine Learning Engineer

• Designing and implementing seamless integration solutions and robust platform architectures to optimize technology infrastructure
• Analyzing business requirements and developing technical designs to enable successful delivery of innovative technology solutions
• Leading strategic technology planning and promoting technological advances to create environments where people and technology thrive together
• Overseeing multiple projects and maintaining executive-level client relations to drive business growth and shape client engagements
• Mentoring the next generation of leaders and cultivating potential within teams to uphold PwC's reputation for quality and integrity
• Conducting research and feasibility studies to identify gaps in the market and convert opportunities into success for the firm
• Developing new ideas, solutions, and structures to drive thought leadership and enhance business performance
• Collaborating across the PwC Network to leverage collective strength and maximize client satisfaction

AI Architect Director

• 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

• Developing innovative software solutions to drive digital transformation and enhance business performance
• Designing, coding, and testing applications to deliver exceptional user experiences
• Utilizing Acceptance Test Driven Development (ATDD) and Continuous Integration/Continuous Delivery (CI/CD) methodologies to improve software quality
• Managing application lifecycle and software project planning to meet client needs
• Implementing API management and microservices for seamless enterprise application integration
• Conducting code reviews and debugging to maintain coding standards and software reliability
• Collaborating with clients to analyze business requirements and deliver tailored solutions
• Applying analytical thinking and problem-solving skills to address complex software engineering challenges
• Leveraging programming languages such as Kotlin, Rust, and SwiftUI for application development
• Engaging in business process improvement and requirements management to optimize software solutions

Senior AIT AI Developer

• Designing and implementing seamless integration solutions and robust platform architectures to optimize technology infrastructure
• Analyzing client requirements and developing technical designs to deliver innovative technology solutions
• Leading teams in the successful planning, budgeting, execution, and completion of projects
• Coaching and mentoring team members to leverage their unique strengths and manage performance
• Addressing conflicts and engaging in difficult conversations with clients and stakeholders

AI Architect Manager

• Analyzing and modeling organizational data for the Artificial Intelligence (AI) function to draw business insights
• Applies data extraction, transformation and loading techniques in order to connect large data sets from a variety of sources
• LLM development and fine-tuning strategies, best practices, and standards to enhance AI ML model deployment and monitoring efficiency
• Develop roadmap and strategy for NLP, LLM, Gen AI model development and lifecycle implementation
• Responsible for the design and development of custom ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and retrieval, Retrieval Augmented Generation (RAG)
• Collaborate closely with the MLOps, product teams, business stakeholders, machine learning engineers, and software engineers for the deployment of machine learning models into production environments
• Ensure the use of standards, governance and best practices in ML model development, and adherence to model and data governance standards.

• Lead applications systems analysis, design, and programming activities in alignment with Citi’s enterprise architecture.
• Partner with multiple management teams to integrate systems, deploy new products, and enable process improvements.
• Resolve high-impact, complex problems through deep analysis of business processes, system flows, and industry standards.
• Establish standards for coding, testing, debugging, and implementation.
• Develop a strong understanding of how architecture, infrastructure, and applications integrate to achieve business goals.
• Provide technical mentorship and coaching to mid-level developers and analysts; allocate work as needed.
• Assess and manage risk in all technical and business decisions, ensuring compliance with laws, regulations, internal policies, and ethical standards.
• Design and implement AI-powered testing solutions, including: AI‑generated test cases, Intelligent regression selection, Self‑healing automation, Automated defect and failure analysis.
• Develop and optimize LLM-based tools for: Test data generation and scenario creation, Requirements-to-test traceability, Predictive test failure analysis.
• Embed generative AI capabilities into existing automation frameworks (e.g., Selenium, Playwright).
• Implement advanced GenAI techniques such as prompt engineering and Retrieval‑Augmented Generation (RAG) to enhance testing intelligence.
• Integrate AI-driven testing accelerators into CI/CD pipelines to reduce cycle time and improve stability.
• Support deployment, scalability, monitoring, and optimization of AI models in production environments.
• Contribute to real-time and streaming AI systems that enable continuous testing and rapid feedback loops.
• Ensure compliance with Responsible AI principles, data privacy requirements, governance standards, and quality controls.
• Stay current with emerging trends in generative AI and test automation; evangelize best practices across the organization.
• Mentor junior engineers and QA automation developers on AI-assisted testing methodologies.

Senior Generative AI Developer

• Building AI-powered internal tools and data-driven systems used across the organization - analytics platforms, reporting automation, workflow intelligence.
• Developing AI/ML-driven platforms that process and analyze large-scale operational data from 30M+ sites across our product suite.
• Designing and deploying RAG systems, LLM-powered features, and agentic workflows that generate actionable insights for internal teams.
• Building data pipelines that ingest, transform, and serve behavioral and operational data at scale.
• Prototyping and shipping AI solutions that streamline internal processes, reduce manual effort, and improve decision-making.
• Evaluating and selecting the right models, frameworks, and infrastructure for each use case - balancing performance, cost, and latency.
• Writing clean, well-tested, production-grade Python code with proper error handling, logging, and observability.
• Collaborating with product teams and stakeholders to understand what they need, then building tools they actually use.
• Keeping up with the rapidly evolving AI landscape and bringing new ideas, tools, and approaches to the team.
• Communicating with the team and supporting your peers using chat, audio, and video.

AI Developer

• create artificial intelligence (AI) and machine learning (ML) enabled solutions
• design, create, and implement complete AI systems
• transform client operations and increase data accessibility
• optimize AI and ML systems
• ensure solutions consider broader ecosystem and operating environment
• deepen skill set in software engineering, machine learning operations (MLOps), and software deployment

AI/ML Engineer

• The Lead, Network Engineer designs, analyzes, plans and modifies network components supporting customer communication implementation activities.
• Work on problems of diverse scope and complexity ranging from moderate to substantial.
• Be the technical SME for our enterprise WAN, remote LAN/WLAN, and data center SD-WAN network services.
• Responsible for engineering, implementing, and operating a multi-vendor SD-WAN environment, scalable campus/branch switching and wireless, and resilient remote branch solutions.
• Serve as the escalation point for complex routing, overlay, and authentication issues, including RADIUS/TACACS+.
• Provide technical leadership to a team of network engineers.
• Work closely with the Lead Network Architect and Principal Network Architect through structured technical solution peer reviews.

Lead Network Engineer – AI

• Build and maintain shared infrastructure: secure web app hosting infrastructure, MCP/API standards, secret/key management, model selection, guardrails, and a centralized pattern library that all builders across the organization can pull from.
• Own the review, tracking, and advisory process for all corporate AI and automation builds — MCP servers and tool configurations, Claude Skills, API integrations, and agentic workflows — before deployment.
• Partner directly with AI specialists across departments to identify, scope, and build AI and automation solutions that meet high standards for cost, security, and scalability security before they ship.
• Define and maintain the secure intake path for AI and automation requests across the organization, replacing ad-hoc builds and shadow IT with a governed, cost-effective, and scalable process.
• Contribute to the organization’s AI cost efficiency by monitoring, alerting, and setting thresholds on AI spend across usage-based tools and platforms.

AI & Automations Lead

• Lead the development and implementation of quality management frameworks across Supply Chain Technology systems, ensuring compliance, reliability, and performance
• Oversee digital quality programs, including system validation, testing, and continuous improvement initiatives aligned with business and regulatory requirements
• Drive the integration of AI and advanced analytics into quality systems to enhance monitoring, predictive insights, and process optimization
• Establish and monitor quality metrics, dashboards, and scorecards to evaluate system performance and identify opportunities for improvement
• Partner with cross-functional teams (IT, Supply Chain, Quality, Regulatory) to ensure alignment on quality standards and execution priorities
• Lead risk management activities, including assessment of system controls, mitigation strategies, and issue escalation processes
• Guide process improvement initiatives, leveraging methodologies such as Lean, Six Sigma, or continuous improvement practices
• Ensure adherence to regulatory and compliance standards, including auditing systems, interpreting regulations, and implementing required changes
• Manage and develop a team, providing coaching, performance feedback, and career development support
• Foster a culture of quality, innovation, and accountability aligned with organizational values and business goals.

Machine Learning Engineer jobs in Florida

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AI Legal Engineer

AI Workflow Engineer

Senior Software Engineer – Applied AI

Licensed Mortgage Loan Originator

Product Engineer – AI

Machine Learning Engineer

AI/ML Engineer – Full Stack Developer

Staff Machine Learning Engineer

AI Architect Director

AI Engineer

Enterprise AI Architect

Senior AIT AI Developer

AI Architect Manager

AI Engineer

Senior Generative AI Developer

AI Developer

AI/ML Engineer

Lead Network Engineer – AI

AI & Automations Lead

Technology Manager, Quality Systems – AI Lead