AI Developer & Data Engineer — Financial Advisory AI Platform

Job Title: Senior AI/ML Engineer

Location : Remote

Experience : 7+ yrs

Note : Candidates must have product background and an experience of atleast 4 years in banking domain

We are seeking a senior AI Developer and Data Engineer to design, build, and operationalise an Azure OpenAI-powered financial advisory platform serving 15 AI use cases — including Spend Advisor, Balance Prediction, Financial Health Score, Savings Automation, Goal Planning, and What-if Simulation. You will own both the LLM engineering stack (prompt architecture, guardrails, private endpoint security, content safety) and the financial data layer (transaction pipelines, feature engineering, RAG knowledge base, analytics) that makes the AI system accurate, explainable, and safe for direct consumer use in a regulated banking setting.

KEY RESPONSIBILITIES

Azure OpenAI Framework & LLM Engineering

Design and implement the end-to-end Azure OpenAI deployment architecture — private endpoint, Managed Identity, RBAC, AKS integration, GPT-4o deployment type selection (Standard / PTU / Global), and TPM quota management.

- Build the Business Logic + Prompt Builder layer: prompt templates, chain-of-thought patterns, and system prompt configurations for financial advisory use cases.
- Implement RAG pipelines using Azure AI Search and vector stores (text-embedding-3-large) to ground financial advice in live user transaction data.
- Design and implement the Validation Layer (Guardrails): out-of-scope detection, hallucination filtering, financial misinformation blocking, and Retry/Reject logic.
- Configure Azure OpenAI Content Filters and custom blocklists aligned to PCI DSS 4.0, GDPR, and banking AI governance requirements.
- Implement human-in-the-loop controls for high-impact use cases (Savings Automation, Goal Planning) and confidence scoring on all financial recommendations.

Data Engineering & Analytics

*esign and build real-time and batch pipelines ingesting transaction data from Oracle DB on Azure, Azure Event Hub, and Kafka-on-AKS into the AI context layer.





- Engineer financial features per use case: rolling spend aggregations (MCC-level), time-series features for balance forecasting, anomaly detection signals for Smart Nudge, and composite Financial Health Score components.
- Build and maintain the RAG knowledge base: curate, chunk, embed, and index financial product data and spending benchmarks into Azure AI Search.
- Implement PII masking, column-level encryption (Key Vault-managed keys), and full data lineage from Oracle source through feature store to AI context payload.
- Build AI performance analytics: response accuracy dashboards, model input drift detection, bias monitoring feeds, and Sentinel log ingestion volume reports.

Security, Compliance & Governance

*nforce no-public-internet AOAI access — Private Endpoint, VNet integration, NSG rules, DNS private zones. No API keys in code — Managed Identity only.

- Implement audit logging for all AI interactions (input, output, model version, latency, user context) to Azure Log Analytics with PII masking.
- Own HIGH-priority AI Risk Assessment remediation: disclaimers, user feedback mechanism, accuracy benchmarking across all use cases, and cross-functional deployment gate.
- Maintain Azure ML model registry, model versioning, deployment approval pipeline, and model retirement process aligned to Azure OpenAI deprecation lifecycle.

TECHNICAL SKILLS

Azure OpenAI Prompt Engineering

RAG / Vector Search

LangChain / Semantic Kernel

Azure AI Search

Content Safety APIs

Guardrails / NeMo

Azure ML Pipelines

Python (PySpark / ML)

Azure Databricks

Apache Kafka

Azure Event Hubs

Oracle DB/Reddis

Azure Data Factory

Delta Lake / Parquet

Redis / Caching

Azure Kubernetes Service

Private Endpoints / Vnet

Managed Identity / RBAC

Azure Key Vault





Azure Monitor / KQL

CI/CD (Azure DevOps)

PII Masking / Tokenisation

PCI DSS / GDPR

REQUIREMENTS

*+ years of experience in AI/software development, with at least 4 years building and shipping production LLM or generative AI applications.
- Deep hands-on experience with Azure OpenAI Service — deployment configuration, prompt engineering, RAG pipelines, and content safety controls.
- Strong data engineering background: real-time streaming (Kafka / Event Hub), financial-scale batch pipelines, feature store design, and SQL/PySpark.
- Experience deploying AI and data workloads on AKS with private networking, Managed Identity, and enterprise security controls.
- Proven track record implementing guardrails, safety layers, and compliance controls for AI systems in regulated (financial services, banking, or healthcare) environments.
- Experience with MLOps: model versioning, performance monitoring, drift detection, and CI/CD pipelines for AI deployments.
- Strong understanding of PCI DSS 4.0, GDPR Article 22 (automated decision-making), and banking AI governance frameworks.
- Azure certifications preferred: AI Engineer Associate (AI-102), Data Engineer Associate (DP-203), or Azure Developer Associate (AZ-204).
- Bachelor's or Master's degree in Computer Science, Data Science, Software Engineering, or equivalent experience.

The selected candidate will be responsible for implementing AI features such as:

Spend Advisor

Financial Health Score

Balance Prediction

Smart Financial Nudges

Savings Automation

Goal Planning

Category Optimization

Spending Behaviour Analysis

Budget Recommendation

What-if Financial Simulation

Recurring Spend Detection

Savings Consistency Analysis

Lifestyle Insights

Emergency Fund Planning

Income vs Expense Analysis

Success Criteria The candidate should be able to deliver production-ready AI solutions that provide personalized financial recommendations, predictive insights, budgeting assistance, savings guidance, and intelligent financial planning integrated with our digital banking platform.

📌 AI DATA ENGINEER BANKING (India)
🏢 Ekloud
📍 India