About the project
We are seeking an experienced AI/ML Engineer to lead the design and delivery of a production-grade, multi-agent AI platform for enterprise use cases in ITSM and HR. This is a hands-on, technical leadership position that combines deep expertise in large language models (LLMs), retrieval-augmented generation (RAG), hybrid search/retrieval systems, and classical ML approaches such as clustering, anomaly detection, and root-cause analysis.
The successful candidate will own end-to-end architecture — from system design and deployment to performance optimization — partnering closely with Product, Platform, and Security teams to ship secure, reliable, and scalable features for multi-tenant environments.
Your duties
As an AI/ML Engineer you will be responsible for:
- Architect and deliver multi-agent orchestration systems with planning, tool-use, agent-to-agent protocols, and memory management, ensuring tenant isolation and governance
- Build high-reliability RAG pipelines including chunking, indexing, hybrid retrieval (BM25 + vector), query rewriting, re-ranking, and grounding with citations
- Develop online inference workflows for LLM and classical ML models, maintaining SLOs for latency, cost, and quality
- Design hybrid enterprise search solutions across structured and unstructured data (documents, tickets, HRIS/ITSM systems, logs, metrics)
- Implement and optimize embeddings, ANN indices, query planning, and feedback-driven re-ranking
- Develop and productionize classical ML models for anomaly detection, clustering, incident classification, and root-cause analysis
- Establish evaluation and safety frameworks, including automated regression testing, red-team testing, and compliance with SOC2/ISO and regional data policies
- Implement guardrails for prompt-injection, PII protection, RBAC, and content filtering
- Mentor engineers, lead design reviews, and collaborate with PMs and Design on roadmap priorities and success metrics
- Partner directly with enterprise clients on architecture reviews, scalability planning, and performance optimization
Requirements
- 5+ years of experience building production-grade ML/AI systems, with at least 3 years shipping LLM or RAG-based systems at scale
- 3+ years of experience leading distributed, multi-service architectures
- Expert proficiency in Python and Java, with strong foundations in algorithms, data structures, and distributed systems
- Proven delivery experience with RAG architectures using hybrid retrieval, vector databases (FAISS, Milvus, Pinecone, Weaviate), and Elasticsearch/OpenSearch
- Hands-on experience with agent frameworks such as LangGraph, LangChain, AutoGen, or CrewAI
- Proficiency in classical ML techniques including anomaly detection, clustering, and supervised classification
- Familiarity with MLOps and infrastructure tools such as Kubernetes, Docker, MLflow, W&B, Prometheus, and Grafana
- Experience building secure, multi-tenant systems on AWS, Azure, or GCP
- Integration experience with enterprise systems like ServiceNow, Jira, Workday, or SuccessFactors
- Excellent command of English, both written and spoken, sufficient for professional communication in international and technical environments.
- Strong collaboration skills and ability to engage with cross-functional teams (Product, Security, Data Engineering)
Nice to have:
- Experience with graph-based reasoning or topology for root-cause analysis (Neo4j, Neptune)
- Familiarity with retrieval safety, differential privacy, and policy enforcement
- Advanced evaluation knowledge (RAGAS, DeepEval, or LLM-as-judge frameworks)
- Expertise in large-scale vectorization, caching, and cost optimization strategies
- Open-source contributions to LLM, RAG, search, or MLOps frameworks
About team up
At Team Up, we empower top professionals to build remote careers with international companies, all while working from their homelands. Since 2020, we've connected over 500 talents with global companies, creating opportunities that bridge borders and fuel local growth. What began as a partnership between Georgia and Germany has now expanded to 7 countries, driven by a shared vision of connection, growth, and a better future for work.
Benefits and perks of remote career with Team Up
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