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Job Description:

Job Description: Product Architect - GenAI/AI-ML Engineering

Customer-Focused Problem Solving with Practical AI Implementation

Position Overview

We are seeking a Senior Architect with a strong QA mindset and hands-on engineering expertise to drive customer value through strategic application of Generative AI and Machine Learning technologies. This role requires a pragmatic approach to AI implementation—focusing on solving real customer problems and product pain points rather than superficial AI integration for marketing purposes.

The ideal candidate will work across multiple enterprise DevOps product lines (Agile Requirements Designer, Service Virtualization, Test Data Manager, Nolio Release Automation and Continuous Delivery Director) to identify opportunities where AI/ML can deliver measurable business value, design and implement solutions, and ensure quality through comprehensive testing and validation.

Key Responsibilities

1. Customer Problem Identification & Solution Design 

·Analyze customer feedback, support tickets, and product usage data to identify pain points and opportunities for AI/ML intervention

·Conduct root cause analysis of product issues and design AI-powered solutions that address underlying problems

·Design end-to-end solutions that integrate AI/ML capabilities seamlessly into existing product workflows

·Create proof-of-concepts (POCs) to validate AI solutions before full implementation

·Measure and demonstrate ROI of AI implementations through quantifiable metrics (time savings, error reduction, user satisfaction)

2. Hands-On Development & Implementation 

·Write production-quality code across multiple technology stacks (Java, Python, TypeScript, C++)

·Implement AI/ML models using frameworks like LangChain, LangGraph, OpenAI APIs, and custom ML pipelines

·Integrate AI capabilities into existing microservices and monolithic applications

·Build APIs and services that expose AI functionality to product features

·Develop data pipelines for training, inference, and model management

·Code reviews and technical leadership for AI/ML implementations

3. Quality Assurance & Testing 

·Design comprehensive test strategies for AI/ML systems including:

  • Unit tests for AI model wrappers and data processing

  • Integration tests for AI service endpoints

  • Performance and load testing for AI inference pipelines

  • Accuracy and validation testing for model outputs

  • A/B testing frameworks for model comparison

·Implement automated testing for AI features to ensure reliability

·Validate AI outputs for correctness, bias, and edge cases

·Monitor AI system performance in production and establish alerting

4. Architecture & Technical Leadership 

·Define AI/ML architecture patterns and best practices for the organization

·Create technical documentation for AI implementations

·Mentor engineers on AI/ML best practices and pragmatic implementation approaches

·Evaluate and select AI/ML tools and frameworks based on technical merit and business value

·Design scalable AI infrastructure that can handle production workloads

Required Technical Expertise

Core Programming Languages

·Java 17+ (Spring Boot 3.5+, microservices architecture)

·Python 3.10+ (AI/ML development, data processing)

·TypeScript/JavaScript (Angular 19, React, Node.js)

·SQL (complex queries, database optimization)

AI/ML Technologies & Frameworks

·Generative AI:

  • LangChain and LangGraph for multi-agent workflows

  • OpenAI API, Anthropic Claude, or similar LLM APIs

  • Prompt engineering and optimization

  • RAG (Retrieval-Augmented Generation) implementations

  • Vector databases and embeddings

·Machine Learning:

  • Scikit-learn, pandas, NumPy for traditional ML

  • Model training, evaluation, and deployment

  • Feature engineering and data preprocessing

  • Model versioning and MLOps practices

·AI/ML Infrastructure:

  • Model serving and inference pipelines

  • API design for AI services

  • Performance optimization for AI workloads

  • Cost optimization for AI API usage

Enterprise Technology Stack

·Backend Frameworks:

  • Spring Boot 3.5+ (Java microservices)

  • Grails 5.3+ (legacy system maintenance)

  • RESTful APIs and GraphQL

·Frontend Technologies:

  • Angular 19+ (modern web applications)

  • React (component-based UI)

  • TypeScript, JavaScript (ES6+)

  • Webpack, Vite, or modern build tools

·Database & Data Management:

  • PostgreSQL, MySQL, MSSQL, Oracle

  • MongoDB (NoSQL)

  • Database schema design and optimization

  • Data migration and ETL processes

·DevOps & Infrastructure:

  • Docker and containerization

  • CI/CD pipelines (Jenkins, GitLab CI)

  • Gradle, Maven (build automation)

  • Kubernetes (container orchestration - preferred)

·Authentication & Security:

  • Keycloak, OAuth2, JWT

  • Security best practices for AI systems

  • Data privacy and compliance (GDPR, PII handling)

Testing & Quality Assurance

·Testing Frameworks:

  • JUnit, TestNG (Java)