Quantiphi
Architect MLE
USA - Remote
Sponsorship not specifiedDetected 7 hours ago
PythonReactFastAPIDistributed SystemsGitSnowflakeVector DatabasesAWSGCPAzureCloud PlatformsDockerKubernetesTerraformCI/CDGitHub ActionsJenkinsDevOpsMachine LearningTensorFlowPyTorchData EngineeringNLPLLMs
> stay_score
odds of building a lasting career here
40Risky
Cap-exempt (no lottery)0
Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
Thin sponsorship signal and lottery-bound. A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.
Lottery odds assume a STEM candidate.
Personalize to your clock →> community_outcomes
No reports yet — be the first to help the next applicant.
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.
Responsibilities
- 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 custom agent-tools and define specialized agent-skills that empower agents to perform complex, domain-specific tasks.
- Implement advanced context engineering and memory systems to ensure agents maintain state, learn from interactions, and make informed decisions in dynamic environments.
- 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:
- Architect & Build Agentic Systems: Design and develop end-to-end multi-agent systems from scratch.
Requirements
- Programming & ML: Expert-level Python proficiency with ML frameworks (TensorFlow, PyTorch, Transformers).
- Hands-on experience with vector databases (Pinecone,
- Experience with LLM application monitoring tools (LangSmith, Weights & Biases, custom telemetry solutions) Proven ability to architect and implement complex
- AI systems from scratch in production environments Cloud Platform Expertise:
- Production-level experience with at least one major cloud platform (AWS,
- Must Have: Programming & ML: Expert-level Python proficiency with ML frameworks (TensorFlow, PyTorch, Transformers).
Nice to have
- Safety and abuse:
- Model lifecycle:
- Memory and state:
Skills
- Hands-on evals for agents:
- trajectory / tool-use checks, golden traces, LLM-as-judge with fixed rubrics, regression suites.
- Online evals, drift thinking, and clear quality gates before or after deploy (thresholds, alerts, rollback criteria).
- prompt injection via tools, untrusted retrieval, PII handling in prompts and logs, allowlists and guardrails.
- Cost and latency discipline:
- budgets per run, timeouts, caps on turns and tool calls.
- routing / gateway patterns, version pinning, fallbacks, and which model for which step.
- what is persisted, retention, redaction, and what must never be stored
- What's in it for YOU at Quantiphi:
- Make an impact at one of the world's fastest-growing AI-first digital engineering companies.
Company info
- We offer first-in-class industry solutions across Healthcare, Financial Services, Consumer Goods, Manufacturing, and more, powered by cutting-edge Generative AI and Agentic AI accelerators.
- Be part of a trailblazing team that's shaping the future of AI, ML, and cloud innovation.
- We are currently looking for exceptional individuals who can join us and contribute to a fun, diverse and hybrid work culture.
Apply directly at Quantiphi →Create a free account for alerts like thisView Quantiphi immigration profile
This listing is sourced directly from Quantiphi's careers page and normalized into a canonical job model.