{"id":577100,"url":"https://alion.io/job/irth-solutions-data-science-mlgen-ai-engineer-mid-level-orbit","title":"Data Scientist ML/Gen AI Engineer","company":{"id":7550,"name":"Irth Solutions","domain":"irthsolutions.com","url":"https://alion.io/company/irth-solutions","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":95,"open_postings":4,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":60,"computed_at":"2026-10-29T05:49:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":17500,"max_usd":41000,"period":"year","method":"role_seniority_country_cell","sample_n":39},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"FinOps","optional":false},{"name":"GDPR","optional":false},{"name":"GitHub Actions","optional":false},{"name":"ISO 27001","optional":false},{"name":"Jira","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Power BI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"SOC 2","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Active Directory","optional":true},{"name":"Amazon S3","optional":true},{"name":"AWS","optional":true},{"name":"DynamoDB","optional":true},{"name":"Microsoft Entra ID","optional":true},{"name":"Microsoft Fabric","optional":true},{"name":"MLFlow","optional":true},{"name":"PostGIS","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Time Series","optional":true}],"status":"live","first_seen_at":"2026-09-09T00:00:00Z","employer_posted_date":"2026-09-09","last_verified_at":"2026-10-29T21:05:08Z","board_verified":true,"closed_at":null,"days_open":50,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":50},"description":"About Irth Solutions\nIrth Solutions is a leading provider of cloud-based SaaS software for damage prevention, asset integrity, stakeholder engagement and land management, helping energy, utility, telecom, and infrastructure companies protect their critical network infrastructure. With nearly three decades of industry experience, Irth serves customers across North America and continues to expand its platform with new data-driven and AI-powered capabilities.\nML/GenAI Engineer - Insights (AI/ML)\nLocation: Remote - India\nDepartment: Insights (AI/ML)\nReports to: Data Platform & Analytics Manager\nAbout the Role\nIrth is building a unified and governed Databricks Lakehouse to power cross-product insights and customer-facing data products.\nWe are looking for a hands-on ML/GenAI Engineer who can contribute across the data and ML lifecycle-from establishing reliable, governed data foundations to rapidly prototyping and productionizing machine learning and GenAI solutions.\nYou will work closely with data, platform, product, and domain teams to turn data into measurable customer value across Irth’s key industries:\nDamage Prevention\nAsset Integrity\nLand Management\nStakeholder Engagement\nThe ideal candidate is comfortable working across data engineering, machine learning, GenAI, MLOps, governance, and cloud platforms, with a strong focus on production reliability and business outcomes.\nKey Responsibilities\n1. Build and Strengthen Lakehouse Foundations\nContribute to medallion architecture pipelines (Bronze → Silver → Gold) using Databricks.\nImplement data quality checks, validation gates, and data contracts at ingestion.\nSupport column-level lineage and governance initiatives, targeting at least 95% lineage coverage.\nHelp implement policy-as-code for regional data residency and sensitive-data handling.\nEnsure appropriate PII masking, obfuscation, and access controls across Silver and Gold data layers.\nCollaborate with data engineering and governance teams to improve data reliability, discoverability, and documentation.\n2. Develop and Productionize ML & GenAI Solutions\nExplore, prototype, evaluate, and productionize machine learning and GenAI solutions.\nWork on use cases including:\nForecasting\nAnomaly detection\nNLP\nRetrieval-Augmented Generation (RAG)\nLLM-powered assistants and copilots\nPredictive analytics\nDevelop solutions that address measurable customer and business problems across Irth’s industry verticals.\nPackage and manage models using Unity Catalog model management/registries.\nDesign and implement batch and streaming inference architectures where appropriate.\nPartner with Product and business stakeholders to define success metrics, KPIs, and A/B testing strategies.\nMove successful experiments from prototype to production with clearly defined SLAs, monitoring, documentation, and operational runbooks.\n3. Engineer for Reliability, Scalability & Cost\nBuild production workflows, jobs, and notebooks as infrastructure/assets-as-code using Databricks Asset Bundles (DABs).\nImplement CI/CD pipelines using GitHub Actions.\nDesign reliable, observable, and scalable data and ML workloads.\nWork toward defined operational SLOs, including:\nPipeline success rate: ≥99.5%\n P1 Mean Time to Detect (MTTD): ≤5 minutes\n Mean Time to Repair (MTTR): ≤60 minutes\nImplement proactive monitoring and alerting.\nAutomate incident creation and tracking through Jira where appropriate.\nApply FinOps principles, including resource tagging, workload policies, optimization, and cost monitoring.\nIdentify opportunities to improve compute performance while maintaining cost efficiency.\n4. Advance the Semantic Layer & Data Consumption\nContribute business metrics, definitions, and semantic models to Unity Catalog.\nHelp establish a single source of truth for metrics consumed across BI, analytics, and applications.\nSupport consumption through Power BI and Databricks AI/BI.\nWork with domain teams to develop and maintain trusted data products.\nImprove data-product quality through documentation, contracts, testing, and governance.\nEnsure analytical definitions remain consistent across products and business functions.\n5. Security, Compliance & Auditability by Default\nImplement secure data and ML architectures using RBAC and ABAC within Unity Catalog.\nFollow secure networking practices, including private networking where required.\nManage credentials and secrets using appropriate cloud key-management and secret-management services, such as Azure Key Vault (AKV) or KMS.\nDesign solutions with security, privacy, and auditability built into the development lifecycle.\nSupport compliance requirements across frameworks and regulations such as:\nSOC 2\nISO 27001\nGDPR\nPIPEDA\nProduce and maintain audit evidence related to:\nData lineage\nAccess reviews\nData retention\nSecurity controls\nDisaster recovery (DR) testing and drills\nParticipate in governance and security reviews and remediate identified gaps.\nWhat Success Looks Like\nIn this role, success means you can take a data or AI use case from idea → prototype → production → measurable business impact, while maintaining strong standards for governance, security, reliability, and cost.\nYou will be successful when you:\nDeliver production-ready ML and GenAI capabilities that improve customer outcomes.\nBuild solutions on trusted, governed, and well-documented data.\nMaintain reliable pipelines and inference services against agreed SLOs.\nEstablish strong lineage, data quality, and security practices.\nReduce the time required to move AI experiments into production.\nCreate reusable patterns for ML/GenAI development across Irth’s products and verticals.\nPartner effectively with Product, Data Engineering, Platform, and domain teams.\nRequirements\nQualifications\nRequired Qualifications\n3-6 years of experience in Data Science, Machine Learning, or ML Engineering, with a proven track record of taking models from development through production.\nStrong programming and data skills in Python, SQL, and Spark/PySpark.\nHands-on experience with Databricks, including:\nDelta Lake\nUnity Catalog\nDatabricks SQL (DBSQL)\nJobs and Workflows\nMedallion architecture\nStrong understanding of ML fundamentals, including:\nFeature engineering\nModel training and selection\nModel evaluation and validation\nModel monitoring\nData-quality monitoring\nModel and data drift detection\nPractical GenAI/LLM experience, including:\nPrompt engineering\nRetrieval-Augmented Generation (RAG)\nVector databases/vector stores\nLLM evaluation\nAI safety and guardrails\nUnderstanding of LLM latency, scalability, and cost tradeoffs\nExperience implementing CI/CD for data and ML workloads, including:\nGitHub Actions\nDatabricks Asset Bundles (DABs)\nDEV → QA → PROD environment promotion\nSecrets and configuration management\nExperience with data contracts and data-quality frameworks, including schema governance, automated expectations/testing, validation, and quarantine/error-handling workflows.\nStrong understanding of data security and compliance, including:\nPII handling and protection\nRBAC/ABAC\nData residency requirements\nPolicy-as-code\nStrong communication and collaboration skills, with the ability to work effectively with Product, Engineering, Data, and domain teams.\nAbility to produce clear technical documentation, including Architecture Decision Records (ADRs), runbooks, experiment reports, and operational documentation.\nPreferred Qualifications\nExperience with Microsoft Azure, including:\nAzure Data Lake Storage (ADLS)\nAzure Active Directory / Microsoft Entra ID\nAzure Key Vault (AKV)\nMicrosoft Fabric\nPower BI\nExperience with AWS, including:\nAmazon S3\nAWS KMS\nAWS Secrets Manager\nAmazon RDS\nDynamoDB\nExperience with geospatial data and analytics, including PostGIS, spatial joins, spatial indexing, tiling, and GIS-based feature engineering.\nExperience with streaming and real-time data, including Structured Streaming and Change Data Capture (CDC).\nHands-on experience with MLflow and Unity Catalog Model Serving.\nExperience implementing data and ML observability, including model performance metrics, lineage dashboards, pipeline monitoring, SLA/SLO monitoring, and alerting.\nUnderstanding of FinOps practices, including resource tagging, budgets, cost monitoring, and cost anomaly detection.\nFamiliarity with Disaster Recovery (DR), Business Continuity Planning (BCP), and resilience practices.\nExperience working in utilities, energy, infrastructure, public works, or related industries.\nNice-to-Have Qualifications\nExperience building predictive, risk-scoring, or failure-prediction models for asset integrity, including corrosion, defects, degradation, or infrastructure failure.\nExperience applying anomaly detection and time-series forecasting to pipeline inspection, sensor, maintenance, or operational data.\nExperience engineering ML features from GIS and geospatial asset data, including pipeline routes, facilities, inspection locations, and infrastructure networks.\nExperience developing risk models using pipeline, utility, or asset-integrity 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