Key skills
GenAI expertiseBackend engineeringMicroservices architectureLLM integrationCloud platformsData engineeringMLOps practicesCollaboration skillsProblem-solving skillsTypeScriptPythonJavaGoAIMLGenAILLMOpenAILangChainMLOpsPineconeWeaviateETLData EngineeringAnalyticsReactNext.jsNode.jsAWSAzureGCPSaaSA/B Testing
About this role
Placer.ai is transforming how organizations understand the physical world through its location analytics platform. The Principal Engineer will lead the technical vision and architecture for GenAI product solutions, overseeing the development of AI-powered systems that enhance location data utilization for businesses.
Responsibilities:
- 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
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