Solution Engineer, Data & AI

Arctiq • Brentwood, TN

1d

About The Position

The Solution Engineer, Data & AI is a hands-on builder who designs, delivers, and operates production-grade data and AI solutions. The role is part of a team that applies software engineering discipline to data work, including version control, automated testing, CI/CD, infrastructure as code, and observability. Engineers are also expected to use agentic engineering tools such as Claude Code to accelerate software development while maintaining quality.

Depending on business needs and the engineer’s strengths, the role may focus on either client delivery or internal data and analytics. Client delivery involves fixed-price, outcome-based data and AI projects across industries and problem domains on Azure, AWS, and GCP. Internal data and analytics focuses on integrating, cleaning, and modelling business-system data in Azure Databricks to support trusted reporting and automation of core processes such as quote-to-cash. The two paths share a technical foundation, and engineers may move between them over time.

Requirements

• At least 4 years of experience in software and platform engineering, including meaningful experience on a collaborative software development team.
• Hands-on experience building production data and machine learning solutions in the cloud.
• Strong, demonstrable Databricks platform experience in production environments.
• Practical experience using agentic coding tools to accelerate software development, with examples of how they have changed the way you work.
• Strong proficiency in Python and SQL.
• Solid software engineering fundamentals, including Git workflows, code review, unit and integration testing, and modular design.
• Hands-on experience with CI/CD tooling and infrastructure as code.
• Comfort with containerization, environment management, and automated deployment.
• Working knowledge of Azure, AWS, and GCP, with deep hands-on experience in at least one.
• Understanding of how data platforms are deployed and integrated on each cloud, including storage, identity, networking, and security.
• Familiarity with each cloud’s native data and AI services and when to use them alongside Databricks.
• Deep hands-on experience across Databricks data engineering, governance, and ML/AI capabilities.
• Experience deploying and managing Databricks solutions using software engineering and DevOps best practices.
• Proven ability to design and build scalable batch and streaming data pipelines.
• Strong data modelling skills across dimensional and modern lakehouse patterns.
• Experience with data quality frameworks, testing, and observability.
• Practical experience building, evaluating, and deploying machine learning models to production.
• Experience with generative AI patterns such as retrieval-augmented generation, agents, and LLM evaluation.
• Strong MLOps practices across the full model lifecycle.
• Hands-on proficiency with agentic development tools such as Claude Code.
• Ability to decompose work, write clear specifications, and manage context so agents produce high-quality results.
• Sound judgment about where AI-assisted development adds leverage and where it introduces risk, with a disciplined approach to reviewing and testing its output.

Responsibilities

• Design, build, and maintain reliable, scalable data pipelines for batch and streaming workloads.
• Apply lakehouse architecture patterns, establish data contracts, and manage schema evolution.
• Build data quality checks, tests, and monitoring into pipelines.
• Design analytical data models that reflect business requirements and perform at scale.
• Profile, explore, and analyze data to validate assumptions, surface quality issues, and share insights with stakeholders.
• Apply data governance practices so data assets are secure, traceable, and discoverable.
• Build, train, and evaluate machine learning and generative AI solutions, including classical ML, LLM-based, and agentic applications.
• Establish rigorous model evaluation approaches tied to business-aligned success criteria.
• Move models from experimentation to production and ensure they are reliable, observable, and maintainable.
• Build and maintain CI/CD pipelines for code, data, and models.
• Manage cloud and platform resources through infrastructure as code.
• Implement model lifecycle management, including versioning, deployment, monitoring, drift detection, and retraining.
• Champion code reviews, branching strategies, automated testing, and documentation.
• Use agentic coding tools such as Claude Code in daily work to accelerate design, development, testing, documentation, and refactoring.
• Provide AI agents with effective context through clear specifications, project instructions, and reusable workflows.
• Review, test, and validate AI-generated code and take responsibility for the quality of shipped work.
• Share effective agentic workflows and help turn them into repeatable team practices and accelerators.
• For client delivery: deliver engineering workstreams on fixed-price engagements from technical discovery through outcome acceptance.
• For client delivery: work across Azure, AWS, and GCP client environments, adapting solutions to client cloud, security, and other constraints.
• For client delivery: partner with Architects and the Engineering Manager to meet agreed scope, timeline, and commercial constraints.
• For client delivery: participate in client workshops, demos, and technical working sessions.
• For client delivery: identify scope creep, technical risks, and blockers early and raise them constructively.
• For client delivery: document solutions, transfer knowledge, and help build client-side capability.
• For client delivery: contribute reusable accelerators, templates, and patterns.
• For internal data and analytics: integrate data from business systems such as CRM, PSA, finance/ERP, and HR into Azure Databricks.
• For internal data and analytics: clean, reconcile, and standardize internal data, resolving duplicates, aligning definitions, and establishing a single source of truth for key entities.
• For internal data and analytics: coordinate with internal teams to resolve data quality issues at the source and establish processes to prevent recurrence.
• For internal data and analytics: build documented data models for analytics, KPIs, and executive and operational reporting.
• For internal data and analytics: partner with Finance, Sales, Operations, and leadership to translate reporting needs into reliable data products.
• For internal data and analytics: help design and automate quote-to-cash workflows spanning opportunity, quote, contract, delivery, invoicing, and collections.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed