Solution Engineer, Data – AI

🕒 Outubro 26

🎸 Tennessee – Remoto

🎸 Tennessee – Remote

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

💻 Engenheiro de Soluções

👻 Score fantasma 11%

O que significa: a chance de ninguém estar realmente contratando para esta vaga.Como ler: 60% ou mais significa que achamos que é uma vaga fantasma. Pontuações mais baixas preocupam menos.Por que essa pontuação:

• Aberta há 15 dias.
• As vagas desta empresa costumam ser preenchidas em 29 dias.
• Sem salário divulgado.

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Arctiq

SiteLinkedInTodas as Vagas

201 - 500 funcionários

💼 Consultoria

🏥 Saúde

📦 Logística

Consulting • Healthcare • Logistics

Arctiq é uma empresa que se especializa em fornecer soluções transformadoras de infraestrutura, segurança e engenharia de plataformas. Eles se concentram na segurança empresarial, infraestrutura moderna e engenharia de plataformas, ajudando as empresas a arquitetar soluções práticas e eficientes usando tecnologias de classe mundial. A Arctiq oferece serviços de segurança gerenciada, segurança em nuvem e soluções de infraestrutura moderna, como redes sem fio e infraestrutura de nuvem híbrida. Eles trabalham com vários setores, incluindo saúde, educação, governo e outros, para melhorar a conectividade e fortalecer a segurança usando tecnologias inovadoras. Seus serviços cobrem necessidades abrangentes de infraestrutura e segurança, desde vigilância por vídeo em escolas até iniciativas de cidades inteligentes e soluções nativas da nuvem para indústrias como petróleo e gás.

Descrição

• 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

🎯 Requisitos

• 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

🏖️ Benefícios

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

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