ML / Data Science Lead IRC289285
GlobalLogic Zobraziť všetky práce
- Slovensko
- Trvalý pracovný pomer
- Plný úväzok
You will focus on building practical, production-ready solutions that bridge the gap between unstructured data and structured logic using LLMs, agentic workflows, and knowledge representations (ontologies, knowledge graphs). You are expected to be an “AI-Native” pioneer, utilizing tools like Claude Code, Cursor, Antigravity, and custom MCP servers to automate your own development lifecycle—from data exploration and model evaluation to code generation and documentation.#LI-OM1#LI-RemoteRequirements
- Master’s degree in Computer Science, Data Science, Applied Mathematics, or a related field.
- 7+ years of professional experience in machine learning, data science, or AI engineering. At least 2 years as a Lead developer
- Proven experience as a technical lead or solution architect for ML/AI projects, with accountability for end-to-end delivery in a production environment.
- Strong proficiency in Python and the modern ML/AI ecosystem (e.g., PyTorch, Hugging Face, LangChain/LangGraph, Scikit-learn).
- Hands-on experience with data ingestion, RAG pipeline optimization, model evaluation, deployment (MLOps), and monitoring.
- Deep understanding of generative AI (LLMs, embeddings, RAG, prompt engineering, and agentic reasoning) and its practical constraints (latency, cost, safety, hallucinations).
- Experience turning complex business needs into “machine-ready” technical specifications and acceptance criteria.
- Strong experience with major cloud platforms; hands-on Azure knowledge (Azure AI Search, Azure OpenAI) is highly desirable.
- Evidence of using modern GenAI tools (Claude Code, GitHub Copilot, etc.) to significantly accelerate your development and testing process.
- Ability to lead a team of 4-6 engineers
- Good English to work in a multinational team
- Work with stakeholders to translate domain-specific knowledge into “Spec-Driven” ML architectures and agentic workflows.
- Design and implement solutions that combine the reasoning power of LLMs with the precision of structured knowledge (ontologies/knowledge graphs).
- Pilot the Claude Code CLI and other agentic tools to generate code, run automated tests, and maintain “Context Hygiene” within the project repository.
- Apply structured 4-phase debugging to ML pipelines, focusing on root-cause analysis of hallucinations, retrieval failures, and data drift.
- Define and automate “Skills” (prompt libraries, evaluation scripts, and deployment templates) to be re-used across multiple AI-Native pods.
- Define and implement quality gates, safety metrics, and cost-control practices for GenAI components.
- Lead by example in adopting AI-Native practices, mentoring the AI Apprentice and other team members in the “People + Agents” delivery model.
- Strong problem-solving and follow-up skills; must be proactive and take initiative
- Provide system design recommendations based on technical requirements
- Work independently on development tasks with a minimal amount of supervision
- Leads a team of engineers and AQAs to work on the team backlog, support PO