Solution Engineer, Data – AI

Job not on LinkedIn

🔥 0 minutes ago

🎸 Tennessee – Remote

🎸 Tennessee – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

💻 Solutions Engineer

👻 Ghost score 11%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Arctiq

WebsiteLinkedInAll Job Openings

201 - 500 employees

💼 Consulting

🏥 Healthcare

📦 Logistics

Consulting • Healthcare • Logistics

Arctiq is a company that specializes in providing transformative infrastructure, security, and platform engineering solutions. They focus on enterprise security, modern infrastructure, and platform engineering, helping businesses architect practical and efficient solutions using world-class technologies. Arctiq offers managed security services, cloud security, and modern infrastructure solutions such as wireless networking and hybrid cloud infrastructure. They work with various sectors including healthcare, education, government, and more to enhance connectivity and bolster security using innovative technologies. Their services cover comprehensive infrastructure and security needs, from video surveillance in schools to smart city initiatives and cloud-native solutions for industries like oil & gas.

📋 Description

• Design, build, and maintain reliable, scalable data pipelines for batch and streaming workloads • Apply lakehouse architecture patterns with clear data contracts and managed schema evolution • Build data quality checks, testing, and monitoring into pipelines • Design analytical data models aligned with business requirements and scale • Profile, explore, and analyze data to validate assumptions and surface quality issues • 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 business-aligned evaluation approaches for models • Move models from experimentation to reliable, observable, maintainable production deployments • 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 for design, development, testing, documentation, and refactoring • Provide agents with effective context through specifications, project instructions, and reusable workflows • Review, test, and validate AI-generated code and own shipped quality • Share and codify effective agentic workflows • 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 • For client delivery: partner with Architects and the Engineering Manager within 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 • For client delivery: document solutions, transfer knowledge, and build 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 and establish sources of truth • For internal data and analytics: coordinate resolution and prevention of data quality issues • For internal data and analytics: build documented data models for analytics, KPIs, and reporting • For internal data and analytics: partner with Finance, Sales, Operations, and leadership on reporting needs • For internal data and analytics: design and automate quote-to-cash workflows

🎯 Requirements

• 4+ years in software and platform engineering, including meaningful time on a collaborative software development team • Hands-on experience building production data and ML solutions in the cloud • Strong, demonstrable experience with the Databricks platform in production environments • Practical experience using agentic coding tools, such as Claude Code, to accelerate software development • 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 data platform deployment and integration across storage, identity, networking, and security • Familiarity with native data and AI services across Azure, AWS, and GCP • Deep hands-on Databricks experience across data engineering, governance, and ML/AI capabilities • Experience deploying and managing Databricks solutions using software engineering and DevOps best practices • Ability to design and build scalable batch and streaming 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 ML 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 on AI-assisted development risks and disciplined review and testing of AI-generated output

🏖️ Benefits

• Equal opportunity employer • Inclusive work environment welcoming members of all backgrounds and perspectives • Accommodations or adjustments throughout the interview process and beyond, upon request