Senior AI Developer (Automators)

Luxoft; Poland; Hybrid; PLN 280,000 - 420,000; Full time

Job summary

Luxoft is seeking a Senior AI Developer to design and implement AI-powered internal tools alongside hands-on test automation engineering. You will work on LLM/RAG solutions and translate test engineering problems into practical AI approaches.

The role blends Java/Appium-based automation for Android TV with Python Lambda-based AI pipelines, in a hybrid Poland-based setting. You will contribute to CI/CD, architecture demos, and Confluence documentation.

Qualifications

• Hands-on AWS Bedrock experience with Lambda integration and knowledge bases.
• Practical knowledge of designing AI agents, agentic workflows, and guardrails.
• End-to-end RAG pipeline implementation: chunking, embedding, vector indexing.
• Proficiency in prompt engineering: zero-shot & few-shot with JSON output.
• Familiarity with vector databases such as OpenSearch, Pinecone, or Faiss.
• Ability to reason between Fine-tuning vs. RAG for different problems.
• Experience with LangChain/LangGraph or LlamaIndex for orchestration.
• Understanding embeddings and semantic similarity; Titan Embed or equivalents.
• Strong Python for Lambda functions and AI scripting; Java for test automation.
• Experience with ReportPortal or equivalent test reporting tools.

Responsibilities

• Design and implement AI-powered solutions for automated test triage.
• Develop CBT approaches using LLMs for test case selection.
• Generate test cases from feature specs and Jira/Confluence data.
• Build AWS Lambda functions and API endpoints to integrate AI in CI/CD.
• Apply prompt engineering best practices and evaluate LLM accuracy.
• Maintain automated test suites in Java (Appium/UiAutomator2).
• Triage failures in ReportPortal and integrate results with QMetry (QTM4J).
• Support CI/CD health via Jenkins and nightly/release runs.
• Contribute to framework codebase through fixes and refactoring.
• Document AI architecture and RAG pipelines in Confluence.

Skills

AWS Bedrock

AI agents

RAG pipeline

Prompt engineering

LLM orchestration

Embeddings

Python

Java

ReportPortal

Jenkins/CI-CD

Docker

Tools

OpenSearch

Pinecone

Faiss

QMetry (QTM4J)

LangChain

Confluence

Job description

We are building and maintaining one of the largest OTT platform test automation frameworks, serving millions of customers across streaming TV platforms. The team develops a Java/Appium-based automation framework for Android TV devices and is actively expanding it with AI-powered tooling.

We are looking for a Senior AI Developer. This is a hybrid role combining the design and development of AI-powered internal tools with hands-on test automation engineering skills. The ideal candidate is a software engineer who understands both QA automation and modern LLM/RAG systems - and can translate test engineering problems into practical AI solutions.

Responsibilities:

• Design and implement AI-powered solutions focused on:
  • Automated test failure triage - LLM + RAG pipeline classifying ReportPortal failures (logs, stack traces, screenshots) into structured categories (PRODUCT_BUG, AUTOMATION_BUG, SYSTEM_ISSUE) using AWS Bedrock + Claude
  • AI-based Change-Based Testing (CBT) - LLM-driven test case selection using semantic similarity between code changes and test coverage
  • AI test case generation from feature specs, Jira tickets, and Confluence documentation
• Develop AWS Lambda functions (Python 3.12) and API Gateway REST endpoints to integrate AI capabilities into CI/CD pipelines
• Apply prompt engineering best practices (system prompts, structured JSON output, guardrails) and drive continuous evaluation of LLM solution accuracy
• Use Cursor IDE with MCP integrations, agentic workflows, and context/rules files to accelerate test code generation and maintenance
• Write, maintain, and expand automated test suites in Java (Appium / UiAutomator2) for Android TV platforms
• Develop and maintain functional, regression, NFR, and CBT test suites
• Triage and resolve test failures in ReportPortal; integrate AI triage results with QMetry (QTM4J)
• Support CI/CD pipeline health - participate in Nightly Build, RC, and release automation runs via Jenkins
• Contribute to framework codebase improvements - bug fixes, refactoring, enhancements
• Participate in Kanban ceremonies and PI planning under the ART team
• Present AI solution demos to stakeholders and engineering leadership
• Document AI system architecture, RAG pipelines, and tools in Confluence

Mandatory Skills Description:

• AWS Bedrock - hands-on: model access, Knowledge Bases, Lambda integration (primary AI platform)
• AI agents & Agentic tooling - practical knowledge of designing and operating AI agents, including agentic workflows, reusable skills, rules/guardrails, commands, and multi-tool/multi-agent orchestration
• RAG pipeline - end-to-end implementation: chunking, embedding, vector indexing, retrieval, generation
• Prompt engineering - zero-shot, few-shot, chain-of-thought, structured output (JSON mode), multi-turn
• Vector databases - working knowledge of OpenSearch, Pinecone, or Faiss; understands vector vs. graph DB difference
• Fine-tuning vs. RAG - ability to reason through which approach fits a given problem
• LLM orchestration - LangChain, LangGraph, or LlamaIndex
• Embeddings - understands semantic similarity; experience with Amazon Titan Embed or equivalent
• Python - for Lambda functions, AI pipeline scripting, and data processing
• Java - 3+ years of hands-on test automation development
• ReportPortal or equivalent test reporting tool
• REST API - concepts and hands-on usage
• Jenkins / CI-CD - pipeline debugging and integration
• AWS - S3, Lambda, API Gateway, IAM, OpenSearch Serverless
• Docker - containerized test execution environments

Nice-to-Have Skills Description:

• Cursor IDE advanced features - .cursorrules, memory-bank context files, MCP server integration, and agentic triage workflows
• Android TV platforms - STB / embedded device testing experience (Fire TV, Roku, or similar)
• QMetry (QTM4J) - test management integrated with Jira
• Streamlit - for building internal AI dashboards
• DSPy - programmatic prompt optimization
• AWS SageMaker / MLflow - model evaluation and experiment tracking