Jobs at Irth Solutions

Design and own a unified data model for an AI-driven pipeline risk platform. Responsibilities include lakehouse and medallion architecture, geospatial modeling, entity resolution, ingestion patterns, governance, lineage, data quality, access controls, and traceability. Partner with data engineers, application engineers, and data scientists to implement schemas, pipelines, serving layers, and optimization strategies while maintaining clear architecture documentation.

Build and operate governed MLOps and LLMOps platforms on Databricks. Productionize ML and GenAI models, RAG pipelines, vector search, and inference services; implement CI/CD, data quality, security, lineage, observability, incident response, disaster recovery, and cost controls. Partner with data science, engineering, platform, product, and security teams to ensure reliable, secure, compliant, and cost-effective AI workloads across development, QA, and production environments.

Own and operate AWS, Azure, and Databricks infrastructure supporting data engineering, data science, and MLOps teams. Responsibilities include cloud provisioning, networking, IAM, Unity Catalog administration, security controls, infrastructure as code, platform onboarding, automation, cost management, compliance evidence, troubleshooting, and operational documentation. The role establishes secure DEV, QA, and PROD environments and serves as the primary escalation point for Databricks and cloud platform issues.

Build and maintain batch and streaming data pipelines across AWS, Azure, and GCP using Databricks, Spark, PySpark, SQL, and Delta Lake. Implement medallion architecture, CDC, SCD, data quality, governance, security, metadata, and lineage controls. Configure storage, orchestration, CI/CD, monitoring, troubleshooting, and performance optimization. Collaborate with architects, data scientists, engineers, analysts, and product teams while documenting pipelines, transformations, testing, and operational procedures.

Build governed Databricks Lakehouse foundations and productionize machine learning and GenAI solutions, including forecasting, anomaly detection, NLP, RAG, and predictive analytics. Develop reliable batch and streaming workflows, CI/CD, monitoring, governance, security, lineage, and semantic models. Collaborate with product, data, platform, and domain teams to deliver measurable customer value while meeting reliability, compliance, cost, and operational objectives.

Lead delivery and technical execution for an AI-driven pipeline integrity platform spanning data architecture, engineering, data science, application engineering, and QA. Remain hands-on through coding, design reviews, prototyping, debugging, and quality oversight. Manage compliance, security, audit readiness, release planning, hiring, team development, and cross-functional collaboration. Ensure platform outputs are traceable, reproducible, secure, and delivered predictably against phased milestones.

Leads end-to-end delivery of an integrated asset-integrity and risk platform across Product, Engineering, Data, QA, Infrastructure, Commercial, and Customer Experience teams. Owns schedules, budgets, risks, dependencies, governance, decision logs, executive reporting, phase gates, customer commitments, and launch readiness. The role ensures predictable, auditable delivery without owning product strategy, technical architecture, or the business case.

Build and operate production-grade data ingestion and transformation pipelines in Databricks using Spark/PySpark, SQL, Delta Lake, and medallion architecture. Manage schemas, data quality, deduplication, API integrations, orchestration, monitoring, alerting, recovery, and performance optimization. Collaborate with data scientists and application teams to provide reliable data for LLM/NLP pipelines. Contribute to CI/CD, documentation, data architecture, and onboarding new data sources.

Develop and improve a SaaS platform by designing, developing, and deploying scalable full-stack features. Responsibilities include contributing to architecture and technical decisions, ensuring application quality and security, participating in Agile planning and estimation, conducting code reviews, improving CI/CD practices, and using AI tools to enhance development productivity. The role requires collaboration, technical problem-solving, and continuous learning in a fully remote environment.

Develop and deploy features for a SaaS platform using full-stack development practices. Responsibilities include architectural contributions, application quality and security, Agile sprint participation, code reviews, CI/CD improvement, collaboration, and use of AI tools to enhance development productivity. The role requires Node.js and React expertise, software development experience, Git proficiency, and bilingual French-English communication.

Lead implementation and customer success for pipeline integrity software: configure systems, load and analyze integrity data, manage projects and client relationships, coach users, prioritize product enhancements, and support product teams with customer insights to drive adoption and expansion.

Lead and deliver multiple customer-facing SaaS implementation projects from initiation through closure. Manage plans, budgets, schedules, risks, change control, cross-functional teams, and stakeholder communications. Act as primary customer liaison, track outcomes and ROI, resolve escalations, maintain project documentation and SOPs, and drive process improvements to ensure successful adoption and business outcomes.

Lead end-to-end SaaS implementations: gather requirements, configure and customize software, conduct training, troubleshoot, coordinate UAT, manage stakeholders, document solutions, and support clients through adoption.

Design, prototype, and productionize LLM/NLP and ML solutions using Databricks and Python. Build and improve data pipelines, implement clean modular code, develop REST APIs, collaborate with engineering teams, and ensure models are production-ready with monitoring and testing.

Lead and deliver multiple customer-facing implementation projects for Irth's platform, managing scope, timelines, budgets, risks, resources, and stakeholder communications. Build customer relationships, coordinate internal teams, drive adoption, enforce processes and quality standards, handle escalations, and conduct project closeouts and continuous improvement activities.