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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Architect Machine Learning Engineer - **Company:** Quantiphi, Inc. - **Location:** Princeton, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Amazon Web Services, Amazon Elastic Compute Cloud, Microsoft Azure, Software Code Optimization, Communications Protocols, Computer Programming, Continuous Delivery, Continuous Integration, Database Queries, DevOps, Distributed Systems, Github, Monitoring of Systems, HP Systems Insight Manager, Python (Programming Language), Machine Learning, Software Architecture, Systems Development Life Cycle, Queueing Systems, Tensorflow, Azure Machine Learning, Cloud Platform System, Pytorch, ReactJS, Retrieval-Augmented Generation, Transfer Learning, Large Language Models, Multi-Agent Systems, Prompt Engineering, Reliability of Systems, Git, Cloudformation, Fastapi, Event Driven Architecture, Containerization, Kubernetes, Machine Learning Operations, Terraform, Software Version Control, Api Management, Serverless Computing, Docker, Jenkins, Microservices - **Published:** September 30, 2026 - **Apply:** https://startup.jobs/technical-architect-ml-quantiphi-inc-10228915 ## About the Role * 6-8 years of hands on experience in machine learning and AI engineering with proven track record of taking ML systems to production * Demonstrated expertise in building multi-agent systems and agentic workflows, preferably with Langraph/CrewAI Technical Skills - Must Have: * 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) * 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, GCP, or Azure), including: * Compute services (EC2, GCE, Azure VMs) * Serverless functions (Lambda, Cloud Functions, Azure Functions) * Container orchestration (EKS, GKE, AKS) * Managed AI/ML services (SageMaker, Vertex AI, Azure ML) * Production & DevOps: Strong skills in Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and containerization (Docker, Kubernetes) Technical Skills - Good 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). * 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). ## Description Job Summary: We are seeking an experienced Architect Machine Learning Engineer to architect, build, and deploy production-grade agentic AI systems and multi-agent workflows from the ground up. The ideal candidate will have deep expertise in designing autonomous AI systems that can collaborate, reason, and execute complex tasks with minimal human intervention. 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. Roles & 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. Architectural decisions to ensure: * 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 * 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) * Develop custom tools and plugins that enable agents to interact with various enterprise systems and APIs * Ensure tool reliability, error handling, and seamless integration within agentic workflows * Implement comprehensive monitoring and tracing systems for agent behavior, performance, cost optimization, and latency analysis * Design novel evaluation frameworks to assess multi-step agentic task success, reliability, and accuracy * Utilize advanced observability tools (LangSmith, Arize AI, or custom solutions) to trace agent decision making processes * Establish metrics and KPIs for measuring agentic system performance in production environments