Gen AI Architect

Quantiphi; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Quantiphi is seeking a Senior Machine Learning Engineer to architect, build, and deploy production-grade agentic AI systems for multi-agent workflows. You will design autonomous, collaborative agents with robust memory, tool-calling capabilities, and scalable orchestration across major cloud platforms.

You will integrate LLMs for core reasoning, implement RAG-based retrieval, and ensure observability with a strong focus on CI/CD and MLOps in a remote US environment.

Qualifications

• 6-8 years of hands-on experience in machine learning and AI engineering with production background.
• Expertise in building multi-agent systems and agentic workflows, preferably with Langraph/CrewAI.
• Proficiency in Python and ML frameworks; experience with FastAPI, async programming, and microservices.
• Hands-on with vector databases and scalable RAG systems; cloud deployment on AWS, GCP, or Azure.

Responsibilities

• Architect and build end-to-end multi-agent systems from scratch with agent harnesses.
• Develop agent-tools and agent-skills for complex domain tasks and memory systems.
• Deploy production-grade agentic solutions on major clouds and implement MLOps practices.
• Integrate and optimize LLMs as core reasoning engines for autonomous agents.
• Design RAG systems using vector databases and manage observability and monitoring.

Skills

Python

Multi-agent systems

LLM deployment

Cloud platforms

MLOps CI/CD

Langraph/CrewAI

Tools

Pinecone

Weaviate

ChromaDB

LangSmith

Weights & Biases

Job description

Quantiphi is an award-winning, AI-First global digital engineering company that helps the world’s leading Fortune 1000 organizations transform bold ideas into measurable business impact. We go beyond building innovative AI technologies—we solve the problems that matter most to our clients.

Since our founding in 2013, Quantiphi has built a proven track record of turning complex challenges into meaningful outcomes across industries.

Headquartered in Boston, with more than 4,000 professionals worldwide, we partner with global enterprises to deliver large-scale digital, cloud, and AI-driven transformation. #SolvingWhatMatters.

We are an Elite and Premier partner to Google Cloud, AWS, NVIDIA, Snowflake, and other leading technology platforms, and our work has been recognized across the industry, including:

• 3 AWS AI/ML Partner of the Year awards
• 3 NVIDIA Partner of the Year awards
• 3 Snowflake Partner of the Year awards
• Rated Leaders by Gartner, Forrester, IDC, ISG, Everest Group and other leading analyst firms

Quantiphi delivers First-in-class AI solutions across Life Sciences, Healthcare, Banking, Financial Services, CPG, Manufacturing, Energy, High-Tech, Telecommunications, etc., powered by cutting-edge Generative AI and Agentic AI accelerators.

We are also proud to be certified as a Great Place to Work—reflecting our commitment to our people and our culture.

Experience Level: 7+ years

Employment type: Full Time

Location: Remote - USA

Job Summary:

We are seeking an experienced Senior 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.

Agentic System Architecture & Development:

• 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.

Tool Development & RAG Integration:

• 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

Observability, Monitoring & Evaluation:

• 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

Basic Qualifications:

• 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
• 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:
• Serverless functions (Lambda, Cloud Functions, Azure Functions)
• Container orchestration (EKS, GKE, AKS)
• Production & DevOps: Strong skills in Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and containerization (Docker, Kubernetes)

Other Qualifications:

• 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

What is in it for you:

• Join one of the world’s fastest-growing AI-first digital engineering companies and make a real impact at scale.
• Lead and collaborate with a high-energy team of talented, driven individuals solving complex, meaningful challenges.
• Work with Fortune 500 companies and disruptive innovators in a research-driven environment with 60+ patents.
• Stay ahead of the curve by gaining hands‑on experience with cutting-edge AI, ML, data, and cloud technologies while continuously upskilling.