{"id":738581,"url":"https://alion.io/job/rebeldot-lead-genai-engineer","title":"Lead GenAI Engineer","company":{"id":689309,"name":"RebelDot","domain":"rebeldot.com","url":"https://alion.io/company/rebeldot","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Teamtailor","truth_index":{"grade":"B","score":84,"open_postings":8,"ghost_share":0,"stale_share":0.625,"repost_share":0,"time_to_fill_p50_days":23,"computed_at":"2026-10-29T05:49:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Cluj-Napoca, Romania"],"countries":["RO"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":39000,"max_usd":97000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1286},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Clean Architecture","optional":false},{"name":"Computer Vision","optional":false},{"name":"Context Engineering","optional":false},{"name":"Databricks","optional":false},{"name":"DigitalOcean","optional":false},{"name":"Django","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Google ADK","optional":false},{"name":"Hallucination","optional":false},{"name":"Haystack","optional":false},{"name":"Keras","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"MLFlow","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Spark","optional":false},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-04-15T09:36:54Z","employer_posted_date":"2026-04-15","last_verified_at":"2026-10-29T22:02:41Z","board_verified":true,"closed_at":null,"days_open":197,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":196},"description":"You might be our missing piece if you have:\nStrong Python skills and a solid foundation in software engineering-clean architecture, version control, readable code, and good engineering judgment.\n\nExperience building and shipping production-grade backend applications with FastAPI, Flask, or Django.\n\nA proven track record of designing and scaling ML and/or GenAI systems in production, from data pipelines and deployment to monitoring, optimization, and evaluation.\n\nHands-on experience with GenAI frameworks such as LangChain, LangGraph, ADK, or Haystack.\n\nA good understanding of commercial LLM APIs (such as OpenAI, Anthropic, and similar providers), including where they shine, where they fall short, and how to work with those trade-offs.\n\nStrong experience with RAG systems, including embeddings, vector search, document retrieval, chunking strategies, reranking, and context construction.\n\nExperience designing AI-powered product architectures, including multi-agent systems, tool-using agents, orchestration flows, and inference architectures.\n\nA solid understanding of agentic workflows-planning, memory, tool use, handoffs, and how to make multi-step systems work reliably in practice.\n\nExperience defining and implementing evaluation strategies for GenAI systems, covering retrieval quality, answer quality, task success, latency, cost, and hallucination tracking.\n\nComfort working with relational and non-relational databases, as well as vector stores, and a good understanding of how data should be modeled and prepared for ML and GenAI use cases.\n\nStrong hands-on knowledge of Docker and a practical understanding of containerized workflows. DevOps might own the pipelines, but you know how to work well with them.\n\nExperience building systems that are easy to debug and operate, with solid logging, tracing, monitoring, and observability practices.\n\nGood judgment around guardrails, privacy, and security when building AI systems that interact with sensitive data, users, or external tools.\n\nAn interest in agentic coding and spec-driven development.\n\nExperience leading AI/ML initiatives from idea to production.\n\nA natural tendency to support and mentor others, helping them grow both technically and in the way they approach problems.\n\nExperience with traditional ML or Computer Vision systems that require model architecture design, feature engineering, and dataset management.\n\nWe would be thrilled if you have:\nExperience evaluating and improving agentic systems in real-world settings.\n\nFamiliarity with prompt engineering and context engineering, and a feel for improving consistency, controllability, and tool-calling reliability.\n\nYou have used or implemented MCP servers or clients.\n\nAn eye for research and the ability to turn ideas from papers or experiments into production-ready solutions.\n\nComfort working with cloud infrastructure such as AWS, Azure, GCP, or DigitalOcean, along with a good understanding of CI/CD practices.\n\nHands-on experience with Databricks or Apache Spark.\n\nExperience with observability or evaluation tooling built specifically for LLM-based systems.\n\nThe ability to work comfortably across product, engineering, and research, and help connect the dots between them.\n\nExperience with experiment tracking tools such as MLFlow.\n\nExperience with core ML frameworks such as PyTorch, Keras, or similar.\n\nWe will be working together on the following:\nUnderstanding client needs and shaping AI solutions that are practical, well-architected, and worth building.\n\nDesigning and delivering agentic RAG systems that are reliable, measurable, and ready for production.\n\nLeading major AI initiatives from architecture design to delivery.\n\nDefining evaluation approaches and feedback loops for retrieval, generation, and agent behavior.\n\nDriving good engineering practices around reproducibility, monitoring, observability, scalability, and operational reliability.\n\nHelping shape the guardrails, security practices, and engineering standards behind the AI systems we build.\n\nMentoring and supporting the AI team through pair programming, code reviews, and hands-on collaboration.\n\nKeeping a close eye on what is happening in the field and bringing in new ideas when they genuinely make sense.\n\nUsing the latest AI-assisted engineering practices to build thoughtful, high-quality solutions.\n\nContinuously raising the bar for how we design, build, and deliver AI 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