Gen AI Architect

About Quantiphi: 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. #Solving WhatMatters. 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 awards3 NVIDIA Partner of the Year awards3 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. For more details, visit: Website or LinkedIn Page

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 APIsEnsure 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

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