The most advanced foot traffic analytics platform for anyone with a stake in the physical world.

Engineering Director, GenAI Solutions

Engineering ManagerEngineering ManagerFull TimeRemoteTeam 501-1,000Since 2016H1B No SponsorCompany Site LinkedIn

Location

United States

Posted

56 days ago

Salary

Not specified

Bachelor Degree10 yrs expEnglishAWSAzureCloudETLGoogle Cloud PlatformJavaJava ScriptMicroservicesNext.jsNode.jsPythonReactType ScriptGo

Job Description

• Lead the technical vision and architecture decisions for our GenAI products, ensuring scalability and performance of the AI-powered location intelligence system. • Establish technical roadmaps aligned with business objectives and scalability requirements. • Design and implement end-to-end AI systems from prototype to production, including LLM integration, agentic architectures, and RAG implementations. • Build robust data pipelines and infrastructure supporting AI/ML workloads at scale. • Oversee model deployment, fine-tuning, and optimization for production environments. • Architect scalable microservices and cloud-native solutions supporting real-time AI applications. • Ensure responsible AI practices including guardrails, performance monitoring, and ethical considerations.

Job Requirements

Bachelor's degree or higher in Computer Science, Engineering, or a related field.
10+ years of experience in software development, with 1+ years building production GenAI solutions in B2B SaaS environments.
Expert-level proficiency in backend engineering with Python, Java, Go, or Node.js; proven experience designing microservices architectures and deploying on cloud platforms (AWS, GCP, or Azure). Strong understanding of modern frontend technologies (React/Next.js, TypeScript) and experience architecting full-stack applications with real-time data streaming and WebSocket integrations.
Hands-on experience building and deploying LLM-powered applications using frameworks such as LangChain, LlamaIndex, and APIs from OpenAI, Anthropic, or open-source models.
Experience implementing agentic AI architectures with tool calling, memory systems, and multi-step reasoning capabilities for complex business workflows.
Demonstrated expertise in RAG (Retrieval-Augmented Generation) systems, vector databases (Pinecone, Weaviate, ChromaDB), and semantic search implementations.
Strong foundation in data engineering: designing and optimizing data pipelines, ETL processes, and data infrastructure to support AI/ML workloads.
Proven track record of taking GenAI models from prototype to production, including fine-tuning LLMs, prompt engineering, and implementing guardrails for responsible AI.
Experience with MLOps practices: model versioning, A/B testing, monitoring model performance, and managing the full ML lifecycle in production environments.
Skilled at translating business requirements into technical solutions and collaborating with cross-functional teams including product managers, data scientists, and stakeholders.