Senior Ai Engineer

Senior AI Engineer

Department: Data & Analytics | Reports To: VP of Technology / Director of Engineering | Classification: Full-Time, Exempt

About the Company

We are a high-growth company committed to transforming our industry through intelligent automation and data-driven decision-making. Our team moves fast, builds smart, and measures everything. If you thrive in an environment where your machine learning pipelines and AI products directly drive business outcomes, you’ll fit right in.

Role Overview

We’re looking for a Senior AI Engineer who can hit the ground running. You’ll be a core builder of our AI and ML infrastructure — owning model development end-to-end, operationalizing pipelines in the Microsoft Azure ecosystem, and shipping AI-powered products that real users depend on. That includes designing agentic AI systems with thoughtful human-in-the-loop controls where reliability and accountability matter most. You’ll leverage Azure AI Foundry as a central hub for model experimentation, evaluation, and deployment — bringing together the best of Microsoft’s AI platform to ship reliable, production-grade systems.

This role is ideal for someone who can take a rough problem statement, ask the right clarifying question (just one), and ship — accurately and quickly. You’ll operate with a high degree of autonomy. We’re not a hand-holding environment. We trust our engineers to manage their time, surface blockers early, and deliver with minimal oversight. Speed and accuracy aren’t trade-offs here — they’re both expected.

What You’ll Do

• Design, build, and maintain scalable ML pipelines and model training workflows within the Microsoft Azure stack (Azure AI Foundry, Azure ML, Azure Data Factory, Microsoft Fabric, Synapse Analytics).
• Use Azure AI Foundry to manage the full model lifecycle: prompt engineering, model evaluation, fine-tuning, and deployment of both custom and foundation models.
• Develop, evaluate, and deploy supervised, unsupervised, and generative AI models into production environments.
• Own the full model lifecycle — from feature engineering and experimentation through deployment, monitoring, and retraining.
• Architect and build agentic AI systems: multi-step reasoning pipelines, tool-use patterns, and autonomous task execution with well-defined guardrails — leveraging Azure AI Foundry’s agent framework and orchestration capabilities.
• Design human-in-the-loop (HITL) workflows that keep humans appropriately in control — building review queues, confidence thresholds, escalation logic, and audit trails into AI products from the ground up.
• Collaborate with data engineering, analytics, product, and operations teams to identify high-value AI use cases and translate them into production systems people actually trust and use.
• Build and maintain MLOps infrastructure: model versioning, experiment tracking, automated retraining triggers, and performance monitoring — using Azure AI Foundry’s evaluation and observability tooling where applicable.
• Ensure model accuracy, fairness, and reliability across all production outputs — you own your quality.
• Proactively identify performance bottlenecks, data drift, and architectural improvements before they become incidents.
• Document your work clearly so the team can build on it without asking you to explain it twice.
• Manage your own workload, prioritize effectively, and communicate progress and blockers without being prompted.

What We’re Looking For

Required

• 4+ years of experience in AI/ML engineering, with strong proficiency in Python and SQL.
• Hands-on experience building and deploying production ML models — not just notebooks, but real systems that run reliably at scale.
• Demonstrated experience building AI-powered products or features that end users interact with directly, not just internal tooling.
• Experience designing or contributing to agentic AI systems: orchestration of multi-step tasks, tool/function calling, agent memory, and failure recovery patterns.
• Practical understanding of human-in-the-loop design — confidence scoring, uncertainty quantification, review workflows, escalation logic, and audit logging.
• Proficiency with the Microsoft Azure ML ecosystem (Azure AI Foundry, Azure Machine Learning, Azure Data Factory, Microsoft Fabric, Synapse Analytics) or a demonstrable ability to ramp quickly.
• Solid understanding of ML fundamentals: feature engineering, model selection, regularization, evaluation metrics, and common failure modes.
• Experience with MLOps practices: CI/CD for models, experiment tracking (MLflow or equivalent), model registries, and monitoring in production.
• Ability to work from high-level business requirements and independently drive solutions to completion.
• A track record of delivering accurate, production-quality work under real timelines.
• Self-directed: you manage your own queue, ask smart questions, and don’t wait to be told what to do next.
• Excellent communication skills — you can explain model behavior, agent decisions, and design trade-offs to non-technical stakeholders clearly and without jargon.

Nice to Have

• Hands-on experience with Azure AI Foundry — including the model catalog, prompt flow, AI-assisted evaluation, content safety filters, and the Azure AI Foundry agent service.
• Experience in healthcare, revenue cycle management, or a similarly regulated, data-intensive industry.
• Familiarity with LLM frameworks such as LangChain, LlamaIndex, or the OpenAI/Anthropic SDKs for building agent-based applications.
• Experience with prompt engineering, retrieval-augmented generation (RAG), or fine-tuning foundation models — ideally within Azure AI Foundry or Azure OpenAI Service.
• Familiarity with responsible AI principles: bias detection, explainability (SHAP, LIME), model cards, and governance frameworks — including Azure AI Foundry’s built-in safety and evaluation tooling.
• Exposure to Agile or Scrum workflows.
• Experience with orchestration tools such as Airflow, Prefect, or Azure Data Factory pipelines.
• Familiarity with dbt or similar transformation frameworks for feature pipelines.
• Comfort working in a high-growth, startup-adjacent environment where priorities can shift quickly.

Who Thrives Here

This role is a great fit if you’re someone who:

• Gets more done with less direction — ambiguity energizes rather than paralyzes you.
• Takes ownership of outcomes, not just tasks — you care whether the model and the product it powers actually work in the real world.
• Understands that autonomous AI without the right human controls isn’t a feature, it’s a liability; you design accordingly.
• Is as invested in data quality and pipeline reliability as you are in model performance.
• Can move fast without cutting corners on reproducibility, compliance, or production readiness.
• Wants their work to matter — the AI systems you build have real downstream impact on the business and the people we serve.

We are an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, veteran status, sexual orientation, gender identity, gender expression, or any other characteristic protected by applicable law.