Job Description

Yrs of Exp: 5 - 7

Location-Bangalore

Mode of Work-Work from office (Monday to Friday)

Mode of Interview- First round-Virtual (if you get selected then

Second round-F2F

JD

Role Summary

Lead the architecture, design, and delivery of an AI-powered Fraud & Risk Management platform built on open-source technologies. You'll be a hands-on technical lead with deep expertise in Java, Spring Boot, Hibernate, AWS, and microservices, and you'll embed AI/ML models into real-time risk decisioning while ensuring strong security, compliance, and scalability.

Key Responsibilities

Architecture & Design

Define and own a modular, microservices architecture (API-first, domain-driven boundaries) using Java/Spring Boot/Hibernate.

Establish service contracts, data models, and event flows (Kafka/SQS/Kinesis) for high-throughput risk evaluation.

Drive scalability, resilience, and performance (autoscaling, caching, circuit breakers, rate limiting).

Development & Integration

Build and maintain high-performance services and REST/gRPC APIs; enforce clean code, unit/integration tests, and code reviews.

Integrate with payment processors, core banking/fintech systems, KYC/AML providers, device intelligence, and graph/analytics layers.

Implement observability (CloudWatch/Prometheus/Grafana/OpenTelemetry) and SLOs for latency, availability, and error budgets.

Cloud, Deployment & DevOps (AWS)

Design secure AWS topologies (VPC, subnets, NACLs, Security Groups, IAM), containerize with Docker and orchestrate via ECS/EKS.

Own CI/CD (GitLab/Jenkins/GitHub Actions), blue/green and canary releases, infrastructure as code (CloudFormation/Terraform).

Optimize cost and performance (autoscaling policies, right-sizing, storage tiers).

Security & Compliance (Security-by-Design)

Implement authentication/authorization (OAuth2/OIDC, JWT), RBAC/ABAC for permissions and roles.

Enforce encryption & hashing (TLS 1.2+/1.3, AES-256 at rest, PBKDF2/bcrypt/Argon2 for secrets), secure secrets rotation.

Integrate AWS KMS / HSM for key management; implement comprehensive audit logging and tamper-evident trails.

Champion secure SDLC: threat modeling, SAST/DAST/IAST, dependency scanning, SBOMs, vulnerability remediation.

AI/ML Integration

Partner with data scientists to ingrain AI into the solution: define model-serving interfaces (REST/gRPC), latency budgets, and fallbacks.

Contribute to feature engineering, training data specs, and model evaluation (precision/recall, ROC-AUC, drift detection).

Implement MLOps pipelines (versioning, A/B & shadow tests, monitoring, rollback), and ensure explainability where required (e.g., SHAP/LIME).

Translate model outputs into deterministic risk rules and reason codes for regulator-friendly decisions.

Leadership & Collaboration

Mentor a team of backend engineers; set coding standards, review designs, and resolve complex production issues.

Work closely with Product, QA, DevOps, Data, and Security to deliver roadmap commitments on time with quality.

Document architectures, runbooks, and playbooks; communicate crisply with technical and non-technical stakeholders.

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Required Skills & Qualifications

Core Stack

Java (8+), Spring Boot, Hibernate/JPA, REST/gRPC, Maven/Gradle.

Microservices & distributed systems, API gateway patterns, service mesh (Istio/Envoy) exposure is a plus.

Datastores: PostgreSQL/MySQL, MongoDB, Redis; understanding of query optimization and indexing.

Messaging/Streaming: Kafka/RabbitMQ/AWS SQS/Kinesis.

AWS: EC2, ECS/EKS, Lambda, S3, API Gateway/ALB, CloudWatch/CloudTrail, IAM, Secrets Manager, KMS/HSM.

DevOps: Git, CI/CD (GitLab/Jenkins/GitHub Actions), Docker, IaC (CloudFormation/Terraform), SonarQube, artifact repositories.

Security

Hands-on with OAuth2/OIDC/JWT, RBAC/ABAC, hashing & encryption, audit logging, secrets management, and key rotation.

Familiarity with PCI DSS/SOC 2/ISO 27001 controls or similar financial-grade security frameworks.

AI/ML

Practical exposure integrating ML models into production services (model endpoints, latency SLAs, monitoring).

Understanding of classification/anomaly detection for fraud use cases; basics of drift/feedback loops and feature stores.

Professional

5-7 years total experience with at least 2-3 years in a technical-lead capacity.

Proven delivery of high-availability SaaS in payments/fintech or adjacent high-risk domains.

Strong problem-solving, system thinking, and stakeholder communication.

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Nice to Have

Experience with graph databases/analytics (e.g., Neo4j/Amazon Neptune) for entity linkage.

Knowledge of data pipelines/lakes (Glue, EMR, Athena) and BI tooling for risk analytics.

Certifications: AWS Solutions Architect/DevOps Engineer; security certifications are a plus.

Regards

Valar

Job Type: Full-time

Pay: Up to ?2,400,000.00 per year

Work Location: In person