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Role: Architect Machine Learning Engineer
Experience Level: 8-12 years
Location: US / Canada [ET&CT]
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
Required 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 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).
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
Soft 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
Proven ability to work independently and drive large-scale projects to completion with minimal supervision
Leadership mindset with experience mentoring team members and driving technical excellence
What’s in it for YOU at Quantiphi
Make an impact at one of the world’s fastest-growing AI-first digital engineering companies.
Upskill and discover your potential as you solve complex challenges in cutting-edge areas of technology alongside passionate, talented colleagues.
Work where innovation happens - work with disruptive innovators in a research-focused organization with 60+ patents filed across various disciplines.
Stay ahead of the curve, immerse yourself in breakthrough AI, ML, data, and cloud technologies and gain exposure working with Fortune 500 companies.
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
