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Machine Learning Engineer
В офисеПолная занятостьAI & Machine LearningInvestment Management
A global investment management firm is seeking a Machine Learning Engineer to design and manage AI infrastructure for autonomous agents and enterprise AI solutions. The role involves building production-grade systems, optimizing LLM inference, and guiding teams in AI adoption. Based in Ramat Gan, this on-site position requires strong Python and PyTorch skills, plus experience with distributed systems and container orchestration.
Обязанности
- Lead AI/ML development within the infrastructure group, transforming prototypes into production systems.
- Identify reusable patterns and build modules for LLM routing and inference optimization.
- Provide technical leadership in transitioning development practices to AI-augmented approaches.
- Design and implement AI infrastructure for federated agent development and distributed deployment.
- Manage and orchestrate AI agents to generate architecturally sound code adhering to enterprise standards.
- Build evaluation pipelines and observability frameworks for agentic systems.
- Collaborate with teams to integrate AI tools into workflows and CI/CD pipelines.
- Travel across teams to teach AI infrastructure best practices and guide adoption.
Требования
- Bachelor’s degree with 6+ years, Master’s with 4+ years, or PhD with 2+ years in Computer Science, Data Science, Machine Learning, or related field.
- Experience with AI/ML infrastructure, agent orchestration, or similar AI-powered development tools.
- Proficiency in Python and PyTorch or a similar deep learning framework.
- Strong understanding of enterprise software architecture patterns and distributed systems design.
- Experience with Docker, Kubernetes, and container orchestration in production environments.
- Strong communication and teaching skills, with ability to work across different teams.
Будет плюсом
- Experience with cloud platforms (AWS, Azure, or GCP) and infrastructure-as-code tools.
- Experience with MLOps and production ML workflows, including training pipelines, model versioning, deployment, monitoring, and drift detection.
- Experience building agentic AI systems using frameworks such as LangGraph, LangChain, and PydanticAI.
- Solid foundation in classical machine learning, including supervised and unsupervised learning, feature engineering, and model evaluation.
- Strong knowledge of vector databases, embedding pipelines, and retrieval-augmented generation (RAG) architectures.