Engineering Manager

Required skills

Requirements

6+ years of software development experience
1–3+ years of experience in technical leadership or management
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

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

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

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