Technical Architect - ML

Architect Machine Learning EngineerExperience: 8–12 YearsLocation: New Jersey (Onsite)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 protocolsDynamic task decomposition, sophisticated tool integration, planning mechanisms (ReAct), and self-correction loopsDevelop state management systems and memory mechanisms for persistent agent interactionsEngineer 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 connectionsDesign 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 APIsEnsure tool reliability, error handling, and seamless integration within agentic workflowsImplement 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 accuracyUtilize advanced observability tools (LangSmith, Arize AI, or custom solutions) to trace agent decision making processesEstablish metrics and KPIs for measuring agentic system performance in production environmentsRequired Skills & Qualifications:Experience:6-8 years of hands on experience in machine learning and AI engineering with proven track record of taking ML systems to productionDemonstrated 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 architectureData & Vector Systems: Hands-on experience with vector databases (Pinecone, Weaviate, ChromaDB) and building scalable RAG systemsMonitoring & 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 environmentsCloud 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 optimizationKnowledge of distributed systems, message queues, and event-driven architectures for agent coordinationFamiliarity with SDLC best practices, version control (Git), and agile development methodologiesExperience 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).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 storedIf you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!