Quantiphi

Quantiphi

Architect - ML Engineer

US NJ Princeton · Senior

Sponsorship not specifiedDetected 7 days ago
PythonReactFastAPIDistributed SystemsGitSnowflakeVector DatabasesAWSGCPAzureCloud PlatformsDockerKubernetesTerraformCI/CDGitHub ActionsJenkinsDevOpsMachine LearningTensorFlowPyTorchData EngineeringNLPLLMs

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odds of building a lasting career here

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Cap-exempt (no lottery)0
Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70

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Files H-1B transfers

6 transfer filings in the last year, covering 6 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.

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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.
  • ○ Hierarchical and collaborative multi-agent structures with well-defined agent roles, responsibilities, and communication protocols
  • ○ Dynamic task decomposition, sophisticated tool integration, planning mechanisms (ReAct), and self-correction loops

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
  • 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.
  • Architect & Build Agentic Systems: Design and develop end-to-end multi-agent systems from scratch.
  • Engineer Advanced Agent Capabilities: Develop custom agent-tools and define specialized agent-skills that empower agents to perform complex, domain-specific tasks.
  • We partner with enterprises to reimagine their businesses through intelligent, scalable, and transformative AI driving measurable outcomes at the very core of their operations.
  • As an Elite and Premier partner to leading cloud and AI platforms such as NVIDIA, Google Cloud, AWS, and Snowflake, we build and deliver enterprise-grade AI services and solutions that create real-world impact.

Requirements

  • Expert-level Python proficiency with ML frameworks (TensorFlow, PyTorch, Transformers).
  • Experience with FastAPI, async programming, and microservices architecture
  • Hands-on experience with vector databases (Pinecone, Weaviate, ChromaDB) and building scalable RAG systems
  • 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,
  • Programming & ML: Expert-level Python proficiency with ML frameworks (TensorFlow, PyTorch, Transformers).

Nice to have

  • Hands-on evals for agents: trajectory / tool-use checks, golden traces, LLM-as-judge with fixed rubrics, regression suites.
  • Safety and abuse: 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.
  • Model lifecycle: routing / gateway patterns, version pinning, fallbacks, and which model for which step.
  • Memory and state: what is persisted, retention, redaction, and what must never be stored

Skills

  • 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).
  • 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

Benefits

  • Headquartered in Boston, Quantiphi is a global organization with 4,000+ professionals serving clients across key industry verticals, including BFSI, Healthcare & Life Sciences, CPG, MFG, TME etc.
  • 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.
  • Architect Machine Learning Engineer

Company info

  • Be part of a trailblazing team that's shaping the future of AI, ML, and cloud innovation.
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