Lead Software Engineer - AI & Emerging Technologies

Caterpillar Caterpillar · Industrial · Kosice, Slovakia

Lead Software Engineer role focused on designing, developing, and maintaining software applications, including proofs of concept, with a strong emphasis on emerging AI/ML technologies like transformer models, RAG, agent frameworks, and cloud AI platforms. The role involves evaluating and validating new technologies, writing high-quality code, supporting deployments, and troubleshooting production issues, with a focus on building AI agent systems and optimizing inference.

What you'd actually do

  1. Design, develop, enhance, and maintain software applications, including business-driven proofs of concept (POCs) that assess the feasibility, viability, and value of proposed solutions.
  2. Identify, evaluate, and validate emerging technologies, frameworks, tools, and architectural approaches against business and technical needs.
  3. Gather requirements, compare solution alternatives, and define technical designs, interfaces, and integration patterns across platforms and systems.
  4. Write high-quality code and use automated, functional, performance, volume, and load testing to ensure reliability.
  5. Support deployment and production environments, troubleshoot issues, and monitor bugs through resolution.

Skills

Required

  • Strong experience in software development, architecture, integration, testing, and the software development lifecycle.
  • Ability to translate business and client requirements into practical technical designs and reliable software.
  • Working knowledge of agile delivery, automated testing, application maintenance, and production support.
  • Strong analytical, decision-making, and problem-solving skills.
  • Clear written and verbal communication skills, with the ability to collaborate across technical and business teams.

Nice to have

  • Understanding of modern AI/ML architectures including transformer models, retrieval-augmented generation (RAG), agent frameworks, and model orchestration patterns
  • Hands-on experience with major cloud AI platforms (Azure OpenAI Service, AWS Bedrock, Google Vertex AI) and ability to architect multi-cloud or hybrid solutions
  • Knowledge of MLOps practices including model versioning, deployment pipelines, monitoring, and governance frameworks
  • Experience designing and implementing production AI agent systems using frameworks like LangChain, LangGraph, Semantic Kernel, or similar orchestration tools
  • Understanding of vector databases, embedding models, and semantic search architectures for RAG applications
  • Knowledge of GPU compute infrastructure, model serving platforms (NVIDIA NIM, TensorFlow Serving, vLLM), and inference optimization techniques
  • Familiarity with prompt engineering, fine-tuning approaches, and evaluation frameworks for LLM-based applications
  • Knowledge of emerging AI frameworks including multi-agent systems, autonomous agents, and AI-powered workflow automation

What the JD emphasized

  • Understanding of modern AI/ML architectures including transformer models, retrieval-augmented generation (RAG), agent frameworks, and model orchestration patterns
  • Hands-on experience with major cloud AI platforms (Azure OpenAI Service, AWS Bedrock, Google Vertex AI) and ability to architect multi-cloud or hybrid solutions
  • Experience designing and implementing production AI agent systems using frameworks like LangChain, LangGraph, Semantic Kernel, or similar orchestration tools
  • Knowledge of GPU compute infrastructure, model serving platforms (NVIDIA NIM, TensorFlow Serving, vLLM), and inference optimization techniques

Other signals

  • designing and implementing production AI agent systems
  • understanding of modern AI/ML architectures
  • hands-on experience with major cloud AI platforms