Data Science ML/Gen AI Engineer (Mid-Level)- Orbit

Irth

IndiaremotePosted 1 month ago

Skill Required

ML-EngineeringData-ScienceAI-GenAI-EngineeringData-EngineeringMLOps-EngineeringMid-Level-AI-EngineerAI-ML-Data-EngineerMid-Level-Data-ScientistAI-EngineerMachine Learning EngineerAI EngineerData EngineerAIMachine LearningBackup and Recoverydata engineeringGitHub ActionsEngineeringCD pipelinesStatisticsPrototypingDatabricksISO 27001networkinganalyticalObservabilityPower BIanalyticssecuritybuildingTestNGdesignGitAzureVaultCI/CDUnityFulltime

Key highlights

Required experience: 3–6 years in Data Science, ML, or ML Engineering
Key benefit: Flexible Work Options (Remote - India)
Notable requirement: Hands-on experience with Databricks (Delta Lake, Unity Catalog)
Key benefit: 401(k) Plan with Company Match

Role overview

Irth Solutions is seeking a hands-on ML/GenAI Engineer to contribute across the data and ML lifecycle for their Insights (AI/ML) department. The role involves building a unified and governed Databricks Lakehouse to power cross-product insights and customer-facing data products across industries such as Damage Prevention, Asset Integrity, Land Management, and Stakeholder Engagement. The ideal candidate will work across data engineering, machine learning, GenAI, MLOps, governance, and cloud platforms to turn data into measurable customer value, moving successful experiments from prototype to production with a strong focus on reliability, security, and business outcomes.

Responsibilities

Contribute to medallion architecture pipelines (Bronze → Silver → Gold) using Databricks.
Implement data quality checks, validation gates, and data contracts at ingestion.
Support column-level lineage and governance initiatives, targeting at least 95% lineage coverage.
Help implement policy-as-code for regional data residency and sensitive-data handling.
Ensure appropriate PII masking, obfuscation, and access controls across Silver and Gold data layers.
Collaborate with data engineering and governance teams to improve data reliability, discoverability, and documentation.
Explore, prototype, evaluate, and productionize machine learning and GenAI solutions, including use cases like Forecasting, Anomaly detection, NLP, Retrieval-Augmented Generation (RAG), LLM-powered assistants and copilots, and Predictive analytics.
Develop solutions that address measurable customer and business problems across Irth’s industry verticals.
Package and manage models using Unity Catalog model management/registries.
Design and implement batch and streaming inference architectures where appropriate.
Partner with Product and business stakeholders to define success metrics, KPIs, and A/B testing strategies.
Move successful experiments from prototype to production with clearly defined SLAs, monitoring, documentation, and operational runbooks.
Build production workflows, jobs, and notebooks as infrastructure/assets-as-code using Databricks Asset Bundles (DABs).
Implement CI/CD pipelines using GitHub Actions.
Design reliable, observable, and scalable data and ML workloads.
Work toward defined operational SLOs, including: Pipeline success rate: ≥99.5%, P1 Mean Time to Detect (MTTD): ≤5 minutes, and Mean Time to Repair (MTTR): ≤60 minutes.
Implement proactive monitoring and alerting.
Automate incident creation and tracking through Jira where appropriate.
Apply FinOps principles, including resource tagging, workload policies, optimization, and cost monitoring.
Identify opportunities to improve compute performance while maintaining cost efficiency.
Contribute business metrics, definitions, and semantic models to Unity Catalog.
Help establish a single source of truth for metrics consumed across BI, analytics, and applications.
Support consumption through Power BI and Databricks AI/BI.
Work with domain teams to develop and maintain trusted data products.
Improve data-product quality through documentation, contracts, testing, and governance.
Ensure analytical definitions remain consistent across products and business functions.
Implement secure data and ML architectures using RBAC and ABAC within Unity Catalog.
Follow secure networking practices, including private networking where required.
Manage credentials and secrets using appropriate cloud key-management and secret-management services, such as Azure Key Vault (AKV) or KMS.
Design solutions with security, privacy, and auditability built into the development lifecycle.
Support compliance requirements across frameworks and regulations such as: SOC 2, ISO 27001, GDPR, and PIPEDA.
Produce and maintain audit evidence related to: Data lineage, Access reviews, Data retention, Security controls, and Disaster recovery (DR) testing and drills.
Participate in governance and security reviews and remediate identified gaps.

Requirements

3–6 years of experience in Data Science, Machine Learning, or ML Engineering, with a proven track record of taking models from development through production.
Strong programming and data skills in Python, SQL, and Spark/PySpark.
Hands-on experience with Databricks, including: Delta Lake, Unity Catalog, Databricks SQL (DBSQL), Jobs and Workflows, and Medallion architecture.
Strong understanding of ML fundamentals, including: Feature engineering, Model training and selection, Model evaluation and validation, Model monitoring, Data-quality monitoring, and Model and data drift detection.
Practical GenAI/LLM experience, including: Prompt engineering, Retrieval-Augmented Generation (RAG), Vector databases/vector stores, LLM evaluation, AI safety and guardrails, and Understanding of LLM latency, scalability, and cost tradeoffs.
Experience implementing CI/CD for data and ML workloads, including: GitHub Actions, Databricks Asset Bundles (DABs), DEV → QA → PROD environment promotion, and Secrets and configuration management.
Experience with data contracts and data-quality frameworks, including schema governance, automated expectations/testing, validation, and quarantine/error-handling workflows.
Strong understanding of data security and compliance, including: PII handling and protection, RBAC/ABAC, Data residency requirements, and Policy-as-code.
Strong communication and collaboration skills, with the ability to work effectively with Product, Engineering, Data, and domain teams.
Ability to produce clear technical documentation, including Architecture Decision Records (ADRs), runbooks, experiment reports, and operational documentation.

Nice to have

Experience with Microsoft Azure, including: Azure Data Lake Storage (ADLS), Azure Active Directory / Microsoft Entra ID, Azure Key Vault (AKV), Microsoft Fabric, and Power BI.
Experience with AWS, including: Amazon S3, AWS KMS, AWS Secrets Manager, Amazon RDS, and DynamoDB.
Experience with geospatial data and analytics, including PostGIS, spatial joins, spatial indexing, tiling, and GIS-based feature engineering.
Experience with streaming and real-time data, including Structured Streaming and Change Data Capture (CDC).
Hands-on experience with MLflow and Unity Catalog Model Serving.
Experience implementing data and ML observability, including model performance metrics, lineage dashboards, pipeline monitoring, SLA/SLO monitoring, and alerting.
Understanding of FinOps practices, including resource tagging, budgets, cost monitoring, and cost anomaly detection.
Familiarity with Disaster Recovery (DR), Business Continuity Planning (BCP), and resilience practices.
Experience working in utilities, energy, infrastructure, public works, or related industries.
Experience building predictive, risk-scoring, or failure-prediction models for asset integrity, including corrosion, defects, degradation, or infrastructure failure.
Experience applying anomaly detection and time-series forecasting to pipeline inspection, sensor, maintenance, or operational data.
Experience engineering ML features from GIS and geospatial asset data, including pipeline routes, facilities, inspection locations, and infrastructure networks.
Experience developing risk models using pipeline, utility, or asset-integrity data.
Understanding of regulatory, compliance, and audit-reporting requirements associated with asset integrity and infrastructure analytics.
Experience translating analytical and ML outputs into operational risk indicators, customer-facing insights, or decision-support tools.

Benefits

Competitive Salary – A competitive compensation package based on experience and qualifications.
Medical, Dental, and Vision Insurance – Comprehensive insurance coverage to support you and your family.
401(k) Plan with Company Match.
Generous Paid Time Off (PTO) – Time off to support work-life balance and personal needs.
Company-Paid Holidays – Paid holidays throughout the year.
Flexible Work Options – Work-from-home opportunities are available, depending on role and business needs.
On-Call Compensation – Additional pay for eligible on-call shifts.