Engineering Manager

  • Closed
  • US Company | Large ( employees)
  • LATAM (100% remote)
  • 6+ years
  • Long-term (40h)
  • SaaS Tools
  • Full Remote

Required skills

  • Python
  • AWS
  • LLM
  • Qdrant

Requirements

Must-haves

  • 6+ years of software development experience
  • 3+ years of experience as a Manager or a Tech Lead
  • Experience with LLM-based and agentic AI systems in production (Chatbots, RAG, semantic search, summarization, agentic workflows)
  • Proficiency with Python
  • Proficiency with AWS
  • Proficiency with orchestration libraries (LangChain, LlamaIndex, OpenAI/Bedrock APIs)
  • Experience with vector databases and hybrid search technologies (Qdrant, OpenSearch, Pinecone, etc.)
  • Proven ability to manage technical projects and stakeholders in agile environments
  • Experience leading teams through ambiguity, iteration, and tight feedback loops
  • Strong people leadership skills, including mentorship, system design guidance, and raising engineering maturity
  • Bias toward action and continuous learning—delivering quickly while maintaining quality and robust infrastructure
  • Strong communication skills in both spoken and written English

Nice-to-haves

  • Startup experience
  • Strong understanding of GenAI tech stack (Python, LangChain, LlamaIndex, Qdrant, OpenSearch, AWS)
  • Experience optimizing system performance and supporting team troubleshooting
  • Bachelor’s Degree in Computer Engineering, Computer Science, or equivalent

What you will work on

  • Lead execution and ensure engineering quality across agentic AI systems powering multi-step Copilot workflows
  • Build and scale Retrieval-Augmented Generation (RAG) pipelines and hybrid search infrastructure
  • Develop LLM-based personalization and summarization for legislative, regulatory, and media-related content
  • Design and maintain AI evaluation frameworks measuring accuracy, latency, and user trust
  • Create developer productivity tools enabling fast and reliable AI delivery
  • While participating in architectural discussions and hands-on initiatives, the main focus will be on leading the team, driving technical execution, and establishing repeatable delivery patterns for AI-first development
  • Expected outcomes within the first 12 months:
  • Deliver production-ready GenAI-powered features—Copilot workflows, summarization, agentic systems, and hybrid search—aligned with roadmap priorities and supported by measurable sprint progress
  • Hire and onboard at least one engineer while fostering a culture of ownership, learning, and continuous improvement
  • Build strong partnerships with Product, Design, and GTM teams, translating technical insights into clear input for planning and ensuring engineering outcomes align with user and business goals
  • Expected outcomes within the first 24 months:
  • Define and evolve the long-term engineering strategy for GenAI capabilities, integrating emerging technologies (e.g. multi-agent systems, fine-tuning, improved RAG methods) into production
  • Strengthen technical expertise across the stack (Python, LangChain, LlamaIndex, vector databases like Qdrant or OpenSearch, AWS infrastructure) and support troubleshooting and optimization efforts
  • Improve team velocity through better code reviews, enhanced test coverage, and automated deployments, leading retrospectives that yield measurable process improvements
  • Communicate effectively by clarifying assumptions, escalating blockers promptly, and maintaining transparency throughout project delivery
  • Mentor engineers and contribute to a positive, inclusive, and collaborative culture through initiatives such as onboarding support or guild leadership
  • Support recruitment efforts by conducting interviews, evaluating candidates, and helping identify long-term technical needs