{"id":3295932,"url":"https://alion.io/job/arctiq-solution-engineer-data-ai","title":"Solution Engineer, Data & AI","company":{"id":2029809,"name":"Arctiq","domain":"arctiq.com","url":"https://alion.io/company/arctiq-com","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"BambooHR","truth_index":null},"role":"Solutions","role_family":"Solutions","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Brentwood, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":94000,"max_usd":210000,"period":"year","method":"role_country_seniority_unknown","sample_n":4589},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Databricks","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-26T21:51:35Z","employer_posted_date":"2026-10-26","last_verified_at":"2026-10-28T10:26:50Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Company Overview:\nArctiq is a global, intelligence-driven technology services company delivering professional and managed services across Hybrid Cloud Infrastructure, Networking & Connected Experiences, Cybersecurity, Data & AI, Autonomous Operations & Intelligence, and Enterprise Service Management. We help organizations operate, secure, and modernize complex environments by unifying infrastructure, networking, data, security, automation, and observability under a single, integrated operating model. Our work focuses on helping customers reduce operational friction, improve resilience, and make better, faster decisions as their environments evolve. Arctiq builds on decades of industry expertise and a customer-centric ethos to deliver exceptional value to clients across diverse industries.\nPosition Overview:\nThe Solution Engineer, Data & AI is a hands-on builder who designs, delivers, and operates production-grade data and AI solutions. You will join a team of engineers who bring real software engineering discipline to data work - version control, automated testing, CI/CD, infrastructure as code, and observability are how we work, not afterthoughts. We also expect our engineers to embrace agentic engineering, using tools like Claude Code to dramatically accelerate how software gets built without compromising quality.\nDepending on business need and your strengths, you will be aligned to one of two paths:\nPath 1 - Client Delivery:Support delivery of fixed-price, outcome-based data and AI projects for clients across a wide range of industries and problem domains, on Azure, AWS, and GCP. In a fixed-price model, engineering quality and delivering within agreed scope directly affect client outcomes and project margin.\nPath 2 - Internal Data & Analytics:Bring order to our internal data on Azure Databricks - integrating, cleaning, and modelling data from our business systems so the organization has trusted analytics and reporting and helping automate core business processes such as quote-to-cash.\nBoth paths share the same technical foundation, and engineers may move between paths over time as priorities and interests evolve.\nResponsibilities:\nData Pipeline Engineering\nDesign, build, and maintain reliable, scalable data pipelines for batch and streaming workloads\n\nApply proven lakehouse architecture patterns, with clear data contracts and well-managed schema evolution\n\nBuild data quality checks, testing, and monitoring into every pipeline from day one\n\nData Modelling & Analysis\nDesign analytical data models that reflect business requirements and perform at scale\n\nProfile, explore, and analyze data to validate assumptions, surface quality issues, and share meaningful insights with stakeholders\n\nApply sound data governance practices so data assets are secure, traceable, and discoverable\n\nAI & Machine Learning Engineering\nBuild, train, and evaluate machine learning and generative AI solutions - from classical ML to LLM-based and agentic applications\n\nEstablish rigorous evaluation approaches tied to clear, business-aligned success criteria\n\nTake models from experimentation to production, ensuring they are reliable, observable, and maintainable\n\nMLOps & DevOps\nBuild and maintain CI/CD pipelines for code, data, and models\n\nManage cloud and platform resources through infrastructure as code\n\nImplement full model lifecycle management - versioning, deployment, monitoring, drift detection, and retraining\n\nChampion engineering best practices across the team: code reviews, branching strategies, automated testing, and documentation\n\nAgentic Engineering\nUse agentic coding tools (e.g., Claude Code) as a core part of your daily workflow to accelerate design, development, testing, documentation, and refactoring\n\nProvide AI agents with effective context through clear specifications, project instructions, and reusable workflows\n\nApply rigorous review, testing, and validation to AI-generated code - you own the quality of what ships, regardless of who or what wrote it\n\nShare effective agentic workflows with the team and help codify them into repeatable practices and accelerators\n\nPath 1 - Client Delivery\nDeliver engineering workstreams on fixed-price client engagements, from technical discovery through outcome acceptance\n\nWork across client environments on Azure, AWS, and GCP, adapting patterns to each client’s cloud, security model, and constraints\n\nPartner with Architects and your Engineering Manager to deliver solutions within agreed scope, timeline, and commercial constraints\n\nParticipate in client workshops, demos, and technical working sessions, building credibility with client technical teams\n\nIdentify scope creep, technical risks, and blockers early - and raise them promptly and constructively\n\nLeave clients better off: document solutions clearly, transfer knowledge, and help build client-side capability\n\nContribute to reusable accelerators, templates, and patterns that make future engagements more predictable\n\nPath 2 - Internal Data & Analytics\nIntegrate data from our core business systems (e.g., CRM, PSA, finance/ERP, HR) into our Azure Databricks environment\n\nClean, reconcile, and standardize messy internal data - resolving duplicates, aligning definitions across systems, and establishing a single source of truth for key entities such as customers, projects, contracts, and revenue\n\nCoordinate with internal teams to identify, prioritize, and resolve data quality issues at the source, and put processes in place to prevent them from recurring\n\nBuild trusted, well-documented data models that power internal analytics, KPIs, and executive and operational reporting\n\nPartner with Finance, Sales, Operations, and leadership to understand reporting needs and translate them into reliable data products\n\nHelp design and automate quote-to-cash workflows, connecting data and handoffs from opportunity through quote, contract, delivery, invoicing, and collections\n\nQualifications:\n4+ years in software and platform engineering, including meaningful time on a collaborative software development team\n\nHands-on experience building production data and ML solutions in the cloud\n\nStrong, demonstrable experience with the Databricks platform in production environments\n\nPractical experience using agentic coding tools to accelerate software development - with examples of how it has changed the way you work\n\nTechnical Skills\nSoftware Engineering & DevOps\nStrong proficiency in Python and SQL\n\nSolid software engineering fundamentals: Git workflows, code review, unit and integration testing, modular design\n\nHands-on experience with CI/CD tooling and infrastructure as code\n\nComfortable with containerization, environment management, and automated deployment\n\nCloud Platforms\nWorking knowledge of all three major clouds - Azure, AWS, and GCP - with deep hands-on experience in at least one\n\nUnderstanding of how data platforms are deployed and integrated on each cloud, including storage, identity, networking, and security\n\nFamiliarity with each cloud’s native data and AI services and when to use them alongside Databricks\n\nDatabricks\nDeep, hands-on experience across the Databricks platform, including data engineering, governance, and ML/AI capabilities\n\nExperience deploying and managing Databricks solutions using software engineering and DevOps best practices\n\nData Engineering & Modelling\nProven ability to design and build scalable batch and streaming pipelines\n\nStrong data modelling skills across dimensional and modern lakehouse patterns\n\nExperience with data quality frameworks, testing, and observability\n\nAI / ML & MLOps\nPractical experience building, evaluating, and deploying ML models to production\n\nExperience with generative AI patterns such as retrieval-augmented generation, agents, and LLM evaluation\n\nStrong MLOps practices across the full model lifecycle\n\nAgentic Engineering\nHands-on proficiency with agentic development tools such as Claude Code\n\nAbility to decompose work, write clear specifications, and manage context so agents produce high-quality results\n\nSound judgment on where AI-assisted development adds leverage and where it introduces risk, with a disciplined approach to reviewing and testing its output\n\nArctiq is an equal opportunity employer. If you need any accommodations or adjustments throughout the interview process and beyond, please let us know. We celebrate our inclusive work environment and welcome members of all backgrounds and perspectives to apply.\nWe thank you for your interest in joining the Arctiq team! 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