Spectro Cloud

Technical Lead

Spectro Cloud

full-time

Posted on:

Location Type: Hybrid

Location: San Jose • California • United States

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Job Level

Senior

Tech Stack

AWSAzureCloudGoGoogle Cloud PlatformJavaKubernetesLinuxMicroservicesPythonPyTorch

About the role

  • Design, optimize, and streamline GoLang-based microservices and AI-powered capabilities
  • Build production-grade AI systems - designing, implementing, and maintaining LLM-powered applications, agentic AI workflows, and RAG pipelines across multiple product use-cases
  • Actively participate in guided technical labs covering prompt engineering, vector databases, LLM deployment tooling, multi-agent orchestration, fine-tuning strategies, and evaluation techniques
  • Develop, refine, and operationalize LLM solutions, including prompt design, retrieval strategies, embedding pipelines, LangChain/LangGraph workflows, and API integrations using Python, Hugging Face, FastAPI, and similar frameworks
  • Ensure the seamless operation of our platform through a combination of automation, scripting, and rigorous testing
  • Maintain strong code quality and commitment to producing clean and efficient code
  • Collaborate with cross-functional teams to create scalable, dependable, and secure solutions

Requirements

  • Bachelor's degree in Computer Science or related technical field
  • 8+ years of software development experience (or 6+ years with a Master's degree)
  • Strong LLM/GenAI fundamentals: Solid understanding of large language models, prompt engineering, embeddings, vector search, RAG systems, and lightweight fine-tuning (LoRA/PEFT preferred)
  • Python expertise: Proficiency in Python and hands-on experience with AI/ML libraries such as Hugging Face, PyTorch, LangChain, LangGraph, FastAPI, or similar frameworks
  • LLM deployment experience: Familiarity with Kubernetes-based inference stacks including vLLM, llm-d, TensorRT, PyTorch Serve, or comparable deployment frameworks
  • Proficiency in at least one modern programming language such as Go, Java, or equivalent
  • Solid understanding of containerization and orchestration concepts, including Kubernetes
  • Deep understanding of microservices architecture and REST API design principles
  • Experience designing and building scalable, cloud-native applications
  • Analytical problem-solving: Ability to debug model outputs, improve retrieval accuracy, optimize latency, and iterate quickly through experiments
  • Cloud & AI ecosystem knowledge: Experience with AI/agent frameworks (LangChain, AutoGen, LlamaIndex) and cloud platforms (AWS, Azure, GCP, etc.)
  • Familiarity with virtual machine usage and integration within software solutions
  • Comfortable working in Linux-based environments and using common command-line tools
  • Experience with Cluster-API or deploying AI models to edge devices (NVIDIA Jetson, x86 edge nodes, ARM platforms) is a plus
  • Exposure to Kubernetes-native developer tooling, observability, or MLOps pipelines is a plus
  • Kubernetes certification (CKA or CKAD) is a plus
Benefits
  • Flexible work arrangements
  • Professional development opportunities
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
GoLangPythonHugging FaceFastAPIKubernetesLLMmicroservicesREST APIcloud-native applicationsAI/ML libraries
Soft Skills
analytical problem-solvingcollaborationcode qualitydebuggingoptimizationiteration
Certifications
Kubernetes certification (CKA or CKAD)