[Remote] Solution Engineer, Data & AI

Note: The job is a remote job and is open to candidates in USA. Arctiq is hiring a Solution Engineer, Data & AI to design, deliver, and operate production-grade data and AI solutions. The role applies software engineering practices across data pipelines, analytics, machine learning, MLOps, and agentic engineering, with work in client delivery or internal data and analytics.

Responsibilities

• Design, build, and maintain reliable, scalable data pipelines for batch and streaming workloads
• Apply proven lakehouse architecture patterns, with clear data contracts and well-managed schema evolution
• Build data quality checks, testing, and monitoring into every pipeline from day one
• 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 meaningful insights with stakeholders
• Apply sound data governance practices so data assets are secure, traceable, and discoverable
• Build, train, and evaluate machine learning and generative AI solutions — from classical ML to LLM-based and agentic applications
• Establish rigorous evaluation approaches tied to clear, business-aligned success criteria
• Take models from experimentation to production, ensuring 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 full model lifecycle management — versioning, deployment, monitoring, drift detection, and retraining
• Champion engineering best practices across the team: code reviews, branching strategies, automated testing, and documentation
• Use agentic coding tools (e.g., Claude Code) as a core part of your daily workflow to accelerate design, development, testing, documentation, and refactoring
• Provide AI agents with effective context through clear specifications, project instructions, and reusable workflows
• Apply rigorous review, testing, and validation to AI-generated code — you own the quality of what ships, regardless of who or what wrote it
• Share effective agentic workflows with the team and help codify them into repeatable practices and accelerators
• Deliver engineering workstreams on fixed-price client engagements, from technical discovery through outcome acceptance
• Work across client environments on Azure, AWS, and GCP, adapting patterns to each client’s cloud, security model, and constraints
• Partner with Architects and your Engineering Manager to deliver solutions within agreed scope, timeline, and commercial constraints
• Participate in client workshops, demos, and technical working sessions, building credibility with client technical teams
• Identify scope creep, technical risks, and blockers early — and raise them promptly and constructively
• Leave clients better off: document solutions clearly, transfer knowledge, and help build client-side capability
• Contribute to reusable accelerators, templates, and patterns that make future engagements more predictable
• Integrate data from our core business systems (e.g., CRM, PSA, finance/ERP, HR) into our Azure Databricks environment
• Clean, 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
• Coordinate with internal teams to identify, prioritize, and resolve data quality issues at the source, and put processes in place to prevent them from recurring
• Build trusted, well-documented data models that power internal analytics, KPIs, and executive and operational reporting
• Partner with Finance, Sales, Operations, and leadership to understand reporting needs and translate them into reliable data products
• Help design and automate quote-to-cash workflows, connecting data and handoffs from opportunity through quote, contract, delivery, invoicing, and collections

Skills

• 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 to accelerate software development — with examples of how it has changed the way you work
• Strong proficiency in Python and SQL
• Solid software engineering fundamentals: Git workflows, code review, unit and integration testing, modular design
• Hands-on experience with CI/CD tooling and infrastructure as code
• Comfortable with containerization, environment management, and automated deployment
• Working knowledge of all three major clouds — 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 the Databricks platform, including 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 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 where AI-assisted development adds leverage and where it introduces risk, with a disciplined approach to reviewing and testing its output

Company Overview