Quantiphi

Quantiphi

Technical Architect - MLE

USA - Remote

Sponsorship not specifiedDetected 1 hour ago
PythonReactFastAPIDistributed SystemsGitSnowflakeVector DatabasesAWSGCPAzureCloud PlatformsDockerKubernetesTerraformCI/CDGitHub ActionsJenkinsDevOpsMachine LearningTensorFlowPyTorchData EngineeringNLPLLMs

About the role

  • You will be responsible for creating scalable, robust agentic workflows using cutting-edge frameworks like CrewAI/Langraph, while ensuring enterprise-grade deployment on major cloud platforms.
  • Agentic System Architecture & Development:
  • Hierarchical and collaborative multi-agent structures with well-defined agent roles, responsibilities, and communication protocols

Responsibilities

  • Architect & Build Agentic Systems: Design and develop end-to-end multi-agent systems from scratch.
  • You will create the foundational agent harnesses, define communication protocols, and build orchestration layers using frameworks like CrewAI, Langgraph, and AutoGen.
  • Develop state management systems and memory mechanisms for persistent agent interactions
  • Engineer Advanced Agent Capabilities: Develop custom agent-tools and define specialized agent-skills that empower agents to perform complex, domain-specific tasks.
  • Pioneer Context Engineering: Implement advanced context engineering and memory systems to ensure agents maintain state, learn from interactions, and make informed decisions in dynamic environments.
  • Deploy Production-Grade Solutions: Own the deployment, scaling, and maintenance of robust, low-latency agentic systems on major cloud platforms ( GCP, AWS, or Azure ).
  • You will implement best-in-class MLOps practices for monitoring, continuous integration/continuous deployment (CI/CD), and system reliability.
  • Integrate and Optimize LLMs: Integrate LLMs to serve as the core reasoning engines for autonomous agents. You will apply advanced techniques like RAG and PEFT to optimize performance.
  • Create and maintain comprehensive tool libraries for agents including API integrations, database queries, and external service connections
  • Design and implement RAG systems using vector databases (Pinecone, Weaviate, ChromaDB)

Requirements

  • Programming & ML: Expert-level Python proficiency with ML frameworks (TensorFlow, PyTorch, Transformers).
  • Experience with FastAPI, async programming, and microservices architecture
  • Data & Vector Systems: Hands-on experience with vector databases (Pinecone, Weaviate, ChromaDB) and building scalable RAG systems
  • Monitoring & Observability: Experience with LLM application monitoring tools (LangSmith, Weights & Biases, custom telemetry solutions)
  • Cloud Platform Expertise: Production-level experience with at least one major cloud platform (AWS, GCP, or Azure), including:

Nice to have

  • Experience with prompt engineering techniques, fine-tuning SLMs (PEFT, SFT, RLHF), and model optimization
  • Knowledge of distributed systems, message queues, and event-driven architectures for agent coordination
  • Familiarity with SDLC best practices, version control (Git), and agile development methodologies
  • Experience with tool-calling agents, multi-step workflows, and stateful orchestration (e.g. graphs, planners, routers).

Skills

  • Exceptional problem-solving and analytical thinking with ability to tackle complex, ambiguous challenges
  • Strong communication skills to explain complex agentic concepts to both technical and non-technical stakeholders
  • Leadership mindset with experience mentoring team members and driving technical excellence
  • Our work is rooted in delivering accelerated, quantifiable business value, not just technology for technology's sake.
  • 3x AWS AI/ML award wins.
  • 3x NVIDIA Partner of the Year titles.
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This listing is sourced directly from Quantiphi's careers page and normalized into a canonical job model.