Solution Engineer, Data – AI

This is a fully remote position, open to applicants in Tennessee.

📋 Description

• Design, construct, and maintain dependable, scalable data pipelines for both batch and streaming workloads.

• Implement lakehouse architecture patterns that include clear data contracts and managed schema evolution.

• Integrate data quality checks, testing, and monitoring within pipelines.

• Develop analytical data models that are in line with business requirements and scalable.

• Profile, investigate, and analyze data to validate assumptions and identify quality issues.

• Enforce data governance practices to ensure data assets are secure, traceable, and easily discoverable.

• Develop, train, and evaluate machine learning and generative AI solutions, covering classical ML, LLM-based, and agentic applications.

• Create evaluation methodologies for models that align with business objectives.

• Transition models from experimental stages to reliable, observable, and maintainable production deployments.

• Construct and uphold CI/CD pipelines for code, data, and models.

• Oversee cloud and platform resources through infrastructure as code.

• Implement model lifecycle management, encompassing versioning, deployment, monitoring, drift detection, and retraining.

• Advocate for code reviews, branching strategies, automated testing, and documentation.

• Utilize agentic coding tools such as Claude Code for design, development, testing, documentation, and refactoring.

• Equip agents with effective context through specifications, project instructions, and reusable workflows.

• Review, test, and validate AI-generated code while ensuring the quality of delivered work.

• Share and standardize effective agentic workflows.

• For client delivery: execute engineering workstreams on fixed-price engagements from technical discovery to outcome acceptance.

• For client delivery: engage with Azure, AWS, and GCP client environments.

• For client delivery: collaborate with Architects and the Engineering Manager within defined scope, timelines, and commercial constraints.

• For client delivery: take part in client workshops, demos, and technical working sessions.

• For client delivery: identify scope creep, technical risks, and blockers.

• For client delivery: document solutions, transfer knowledge, and enhance client capability.

• For client delivery: contribute reusable accelerators, templates, and patterns.

• For internal data and analytics: integrate CRM, PSA, finance/ERP, and HR data into Azure Databricks.

• For internal data and analytics: clean, reconcile, and standardize internal data while establishing sources of truth.

• For internal data and analytics: coordinate the resolution and prevention of data quality issues.

• For internal data and analytics: create documented data models for analytics, KPIs, and reporting.

• For internal data and analytics: partner with Finance, Sales, Operations, and leadership regarding reporting needs.

• For internal data and analytics: design and automate quote-to-cash workflows.

⛳️ Requirements

• 4+ years of experience in software and platform engineering, including significant time on a collaborative software development team.

• Practical experience in building production data and ML solutions in the cloud.

• Strong, demonstrable experience with the Databricks platform in production settings.

• Hands-on experience utilizing agentic coding tools, such as Claude Code, to expedite software development.

• High proficiency in Python and SQL.

• Solid foundations in software engineering, including Git workflows, code reviews, unit and integration testing, and modular design.

• Hands-on experience with CI/CD tools and infrastructure as code.

• Comfort with containerization, environment management, and automated deployment.

• Working knowledge of Azure, AWS, and GCP, with extensive hands-on experience in at least one of these platforms.

• Understanding of data platform deployment and integration across storage, identity, networking, and security.

• Familiarity with native data and AI services across Azure, AWS, and GCP.

• In-depth hands-on experience with Databricks across data engineering, governance, and ML/AI capabilities.

• Experience in deploying and managing Databricks solutions using software engineering and DevOps best practices.

• Capability to design and build scalable batch and streaming pipelines.

• Strong data modeling abilities across dimensional and modern lakehouse patterns.

• Experience with data quality frameworks, testing, and observability.

• Practical experience in building, evaluating, and deploying ML models to production.

• Familiarity with generative AI patterns such as retrieval-augmented generation, agents, and LLM evaluation.

• Strong MLOps practices covering the entire model lifecycle.

• Hands-on proficiency with agentic development tools like Claude Code.

• Ability to decompose tasks, write clear specifications, and manage context for agents to produce high-quality results.

• Sound judgment regarding AI-assisted development risks and disciplined review and testing of AI-generated outputs.

🏝️ Benefits

• Equal opportunity employer.

• Inclusive work environment that welcomes individuals from all backgrounds and perspectives.

• Adjustments or accommodations available throughout the interview process and beyond, upon request.