Principal GenAI Engineer

About this role

AI/GenAI Engineer Job Title: AI/GenAI Engineer Experience : 4-7 Reporting To: Solution Architect / Tech Lead Engagement Type: Full-time, Location: Hyderabad Role Overview Seeking an AI / GenAI Engineer to join the AI Core team for the UC-003 Billing Prep program. This role owns the design, development, and integration of AI and generative AI capabilities including reimbursable cost matching, anomaly detection, and in-month billing monitoring built on Google Vertex AI and Gemini. The engineer will work closely with backend and data engineering peers to embed AI-driven automation into the billing preparation pipeline, replacing manual judgement calls with explainable, auditable model decisions. Required Qualifications Technical Skills 4+ years of hands-on experience building and deploying machine learning or AI systems in production: classification, NLP, anomaly detection, or recommendation systems preferred. Proficiency with Google Vertex AI: Vertex AI Workbench, Model Registry, Vertex AI Pipelines, Prediction endpoints, and Feature Store. Experience with Gemini API and Vertex AI Generative AI Studio: prompt engineering, structured output, grounding, and function calling. Strong Python skills: PyTorch, TensorFlow, or scikit-learn for model development; FastAPI or Flask for serving wrappers. Familiarity with GCP data services: BigQuery, Cloud Storage, Pub/Sub used as upstream data sources and downstream output sinks. Understanding of MLOps principles: experiment tracking, model versioning, CI/CD for ML, and production monitoring. Experience Prior experience building AI features in financial, billing, AP, or ERP contexts: cost classification, invoice matching, or spend analytics preferred. Experience integrating LLMs into enterprise workflows with appropriate guardrails, human escalation paths, and auditability. Demonstrated ability to iterate rapidly on model quality based on subject-matter-expert feedback within an agile delivery cadence. Exposure to responsible AI practices: explainability (SHAP, LIME, Gemini grounding), bias evaluation, and model documentation standards. Preferred Qualifications GCP Professional Machine Learning Engineer certification. Experience with Vertex AI Agent Builder or LangChain-on-GCP for agentic workflow patterns. Background in real estate, facilities management, or professional services billing contexts. Familiarity with enterprise AI platform architecture. Knowledge of vector databases (Vertex AI Matching Engine or AlloyDB pgvector) for semantic similarity in reimbursable matching. Key Responsibilities AI Feature Design & Development Design and implement the reimbursable cost matching model—leveraging Vertex AI and Gemini to intelligently classify billing line items as reimbursable or non-reimbursable based on contractual rules, historical patterns, and contextual signals. Build and deploy anomaly detection capabilities to flag billing exceptions, unusual charge spikes, and data quality issues before human review. Develop in-month billing monitoring agents that proactively surface trends, incomplete accruals, and at-risk line items throughout the billing cycle, not just at period-end. Implement Gemini-powered natural language interfaces or copilot features to assist billing analysts during the human review gate (e.g., explain anomaly rationale, suggest classification, surface similar historical cases). Define and instrument confidence scoring and model explainability outputs so reviewers can trust and interrogate AI recommendations. MLOps & Integration Manage the full model lifecycle on Vertex AI: feature engineering, training, evaluation, versioning, deployment to endpoints, and monitoring for drift and degradation. Integrate Vertex AI and Gemini API calls into the backend billing pipeline via well-defined service contracts, ensuring low-latency, fault-tolerant inference. Build feedback loops that capture reviewer accept/reject decisions and corrections to drive continuous model improvement and fine-tuning. Design prompt engineering strategies for Gemini LLM tasks, including few-shot examples, chain-of-thought reasoning patterns, and output schema enforcement. Collaborate with the Backend Engineer on API contracts and the Data Engineer on feature store design, training data pipelines, and ground-truth labelling workflows. Quality, Safety & Governance Define AI evaluation frameworks and offline test suites with precision, recall, and F1 benchmarks for all classification and detection tasks. Implement human-in-the-loop guardrails: ensure no AI decision bypasses the review workbench and that all model outputs are logged with full provenance for audit. Conduct bias and fairness assessments to ensure the model does not systematically misclassify certain vendor, cost centre, or contract types. Document model cards and AI system design decisions to support compliance, audit, and future model handover. Support QA during UAT with test data generation, model stubbing, and scenario validation for edge-case billing patterns.