Senior AI Engineer - ML & Generative AI

Work Location: Sunrise, Florida

Work Mode: Hybrid (3 days/week in office)

Pay Range: $100K/Yr - $120K/Yr Base + Annual Bonus

The posted range is the hiring range for this role - a subset of the broader range available to employees over time - and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate''s skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process

For more information on benefits and what we offer please visit us at US Careers and Benefits

We are seeking a hands-on Senior AI Engineer with a strong foundation in traditional Machine Learning and practical, real-world experience building and deploying LLM- and GenAI-driven systems. This role focuses on designing, engineering, and hardening production-grade AI solutions that are embedded into business workflows-not research prototypes.

You will work in small, high-impact delivery teams (2-3 engineers per initiative) and spend the majority of your time (~70-75%) building systems end to end, while also contributing to solution design, technical decision-making, and cross-functional collaboration.

Software & Systems Engineering

• 10-12 years of overall software engineering experience, including prior work as an ML Engineer or equivalent.
• Strong backend development skills (Python, Java, Node.js, or similar languages).
• Experience designing and building REST or gRPC-based services.
• Solid understanding of distributed system design.
• Containerization and orchestration experience (Docker, Kubernetes).

AI / ML

• Hands-on experience across traditional ML and modern GenAI systems.

• Proficiency with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or equivalents.

• Experience building or deploying:

• ML-driven production systems

• LLM-based applications

• Ability to select ML vs. LLM-driven approaches based on business and operational constraints.

Cloud & DevOps

• Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP).
• Experience with CI/CD pipelines and deployment automation.
• Understanding of model, code, and configuration versioning best practices.

Observability & Production Readiness

• Experience implementing logging, monitoring, and tracing for production systems.
• Familiarity with system resilience patterns such as:
• Rate limiting
• Failover strategies
• Kill-switch mechanisms

Problem Solving & Mindset

• Strong ability to solve ambiguous, real-world engineering problems.
• Comfortable working in fast-moving, iterative environments.
• Ownership mindset with a bias toward practical, scalable solutions.

Communication & Collaboration

• Experience working in cross-functional teams.

• Ability to clearly articulate technical and business trade-offs, including:

• LLM vs traditional ML

• Build vs buy decisions

• Speed vs robustness

AI Solution Design & Problem Solving

• Partner with business and product stakeholders to translate real-world problems into practical AI solutions.

• Determine when to apply:

• Traditional ML approaches (classification, regression, clustering, recommendation systems)

• LLM / GenAI approaches, including agentic workflows

• Evaluate and communicate trade-offs across accuracy, cost, latency, scalability, and operational complexity.

• Design iterative AI workflows and propose alternative solution approaches where applicable.

Hands-on Engineering & Delivery (70-75%)

• Build and own end-to-end AI systems, including:

• Data ingestion and processing pipelines

• Feature engineering and prompt construction

• ML and LLM integration and orchestration

• API-based AI services for downstream consumption

• Deploy and harden production AI systems with:

• Error handling and fallback mechanisms

• Guardrails, safety controls, and exception handling

• Observability (logging, metrics, tracing, dashboards)

• Ensure production readiness through:

• Performance tuning and latency optimization

• Cost management and optimization strategies

• Scalability and reliability planning

• Implement AI system controls such as:

• Input validation and prompt injection mitigation

• Configurable policies and kill switches

• Transition PoCs into production-grade systems through refactoring, testing, and system hardening.

ML & Generative AI Expertise

• Apply strong fundamentals in traditional ML, including supervised and unsupervised learning techniques.
• Build and deploy GenAI solutions, with experience across at least one or two real-world LLM implementations.
• Work with modern LLMs (e.g., OpenAI, Claude, Gemini, Llama or equivalent models).
• Design and implement RAG (Retrieval-Augmented Generation) architectures.
• Apply prompt engineering, evaluation techniques, and iterative optimization.
• Build and evolve tool-based and agentic workflows, including multi-agent systems.
• Use agent orchestration frameworks (e.g., LangChain, LangGraph, or equivalent custom systems).

Collaboration & Technical Leadership (25-30%)

• Act as a senior technical contributor within small delivery teams.

• Debug complex AI system behavior and production issues beyond prompt-level tuning.

• Contribute to architectural and design decisions alongside architects and platform teams.

• Collaborate closely with:

• Product managers and business stakeholders

• Platform, cloud, and infrastructure teams

• Uphold strong software engineering practices and delivery discipline.

AI Solution Design & Problem Solving

• Partner with business and product stakeholders to translate real-world problems into practical AI solutions.

• Determine when to apply:

• Traditional ML approaches (classification, regression, clustering, recommendation systems)

• LLM / GenAI approaches, including agentic workflows

• Evaluate and communicate trade-offs across accuracy, cost, latency, scalability, and operational complexity.

• Design iterative AI workflows and propose alternative solution approaches where applicable.

Hands-on Engineering & Delivery (70-75%)

• Build and own end-to-end AI systems, including:

• Data ingestion and processing pipelines

• Feature engineering and prompt construction

• ML and LLM integration and orchestration

• API-based AI services for downstream consumption

• Deploy and harden production AI systems with:

• Error handling and fallback mechanisms

• Guardrails, safety controls, and exception handling

• Observability (logging, metrics, tracing, dashboards)

• Ensure production readiness through:

• Performance tuning and latency optimization

• Cost management and optimization strategies

• Scalability and reliability planning

• Implement AI system controls such as:

• Input validation and prompt injection mitigation

• Configurable policies and kill switches

• Transition PoCs into production-grade systems through refactoring, testing, and system hardening.

ML & Generative AI Expertise

• Apply strong fundamentals in traditional ML, including supervised and unsupervised learning techniques.
• Build and deploy GenAI solutions, with experience across at least one or two real-world LLM implementations.
• Work with modern LLMs (e.g., OpenAI, Claude, Gemini, Llama or equivalent models).
• Design and implement RAG (Retrieval-Augmented Generation) architectures.
• Apply prompt engineering, evaluation techniques, and iterative optimization.
• Build and evolve tool-based and agentic workflows, including multi-agent systems.
• Use agent orchestration frameworks (e.g., LangChain, LangGraph, or equivalent custom systems).

Collaboration & Technical Leadership (25-30%)

• Act as a senior technical contributor within small delivery teams.

• Debug complex AI system behavior and production issues beyond prompt-level tuning.

• Contribute to architectural and design decisions alongside architects and platform teams.

• Collaborate closely with:

• Product managers and business stakeholders

• Platform, cloud, and infrastructure teams

• Uphold strong software engineering practices and delivery discipline.