Job description

Full-Stack Engineer, AI & Automation Remote | Full-Time ABOUT IMPLENTIO Implentio is building the intelligence layer for modern ecommerce operations. We help DTC and ecommerce brands recover lost money and eliminate operational waste across their parcel and 3PL networks through software, data, and deep operational expertise. Our platform identifies billing errors, uncovers inefficiencies, and transforms messy operational data into clear, high-impact decisions. Behind the technology is a team that has spent years operating inside high-growth ecommerce and logistics environments. We know how complex these systems are because we have run them ourselves. Implentio is backed by leading venture investors and led by a founding team with a track record of building and scaling successful companies. We are building a focused, high-caliber team that values clear thinking, moves quickly, and takes real ownership of outcomes. If you are excited about building software that solves real operational problems for some of the fastest-growing ecommerce brands, we would love to meet you. ABOUT THE ROLE We are hiring a Full-Stack Engineer, AI & Automation to help build the intelligent systems at the core of our products. This is a hands-on, high-ownership role for someone who wants to work at the intersection of applied AI, product engineering, and real-world operational data. You will design and ship LLM-powered features, build automation that replaces manual workflows, and integrate AI into how we detect billing errors, optimize carrier selection, and surface insights for customers. You will work across the full stack, including Python backend services, React frontend, data pipelines, and cloud infrastructure, with AI woven into each layer. Experience in logistics, fulfillment, 3PL, or ecommerce is a plus. WHAT YOU WILL DO • Build and ship AI products. Design, develop, and deliver agentic features end-to-end, including chat-based interfaces, agent workflow experiences, and internal copilots that serve real users in production. • Leverage agentic coding workflows. Use modern agentic coding tools daily, including Claude Code, Codex, Cursor, and similar assistants, to accelerate delivery while maintaining high code quality and engineering standards. • Design agent systems. Architect agent internals, including tool calling, multi-step orchestration, memory and state management, retrieval pipelines, permissions models, and failure recovery strategies. • Own evals and quality. Build and maintain agent evaluation infrastructure, including regression suites, scenario-based tests, golden traces, and both offline and online metrics, so you always know whether your agent got better or worse. • Implement observability. Instrument agents with production-grade observability, including distributed tracing, prompt and version tracking, tool-call telemetry, and cost and latency monitoring. • Own production readiness. Take responsibility for the full lifecycle, including rollout plans, QA, guardrails, incident response, and continuous improvement of deployed agents. WHAT WE ARE LOOKING FOR • Proficiency in TypeScript/Node.js and/or Python, with experience in modern web stacks and startup-pace delivery. • Proven full-stack engineering ability. You have shipped production software end-to-end, not just prototypes or demos. • Shipped LLM-powered features to production. You have owned at least 1 to 2 agentic features or products from design, build, deployment, and operation. • Familiarity with agentic coding best practices. You understand task decomposition, agent delegation, prompt hygiene, iterative loops, and how to review and steer agentic outputs effectively. • Practical experience with agent evals and tracing. • Solid engineering fundamentals. Testing, debugging, performance optimization, and reliability are second nature to you. • Familiarity with agent tooling ecosystems, including prompt and version stores, eval harnesses, and tracing and telemetry platforms such as OpenTelemetry, LangSmith, Datadog, Honeycomb, or similar, is a plus. • Experience with RAG architectures, including vector databases, indexing strategies, chunking approaches, and retrieval evaluation, is a plus. WHAT SUCCESS LOOKS LIKE • You build a strong understanding of our products, workflows, data, and operational constraints quickly. • You ship improvements that make the product smarter, more scalable, and more useful in production. • You apply AI in ways that are practical and durable, not gimmicky. • You become a trusted partner to product and operations by solving messy real-world problems. • You raise the quality of the systems you touch, including code, data models, reliability, and deployment. WHO YOU ARE • You dig into how things work, ask good questions, and do not stop at the surface. • You follow through and ship. Work moves because you move it. • You make sound decisions and practical tradeoffs without needing perfect information. • You care about quality and think through edge cases, testing, and production behavior. • You collaborate well across functions and focus on building what works.

Requirements

Must-haves

Full-Stack Engineer, AI & Automation
Remote | Full-Time
We are hiring a Full-Stack Engineer, AI & Automation to help build the intelligent systems at the core of our products.
This is a hands-on, high-ownership role for someone who wants to work at the intersection of applied AI, product engineering, and real-world operational data.
You will design and ship LLM-powered features, build automation that replaces manual workflows, and integrate AI into how we detect billing errors, optimize carrier selection, and surface insights for customers.
You will work across the full stack, including Python backend services, React frontend, data pipelines, and cloud infrastructure, with AI woven into each layer.
Build and ship AI products. Design, develop, and deliver agentic features end-to-end, including chat-based interfaces, agent workflow experiences, and internal copilots that serve real users in production.
Design agent systems. Architect agent internals, including tool calling, multi-step orchestration, memory and state management, retrieval pipelines, permissions models, and failure recovery strategies.
Implement observability. Instrument agents with production-grade observability, including distributed tracing, prompt and version tracking, tool-call telemetry, and cost and latency monitoring.
Own production readiness. Take responsibility for the full lifecycle, including rollout plans, QA, guardrails, incident response, and continuous improvement of deployed agents.
Proficiency in TypeScript/Node.js and/or Python, with experience in modern web stacks and startup-pace delivery.
Proven full-stack engineering ability. You have shipped production software end-to-end, not just prototypes or demos.
Shipped LLM-powered features to production. You have owned at least 1 to 2 agentic features or products from design, build, deployment, and operation.
Familiarity with agentic coding best practices. You understand task decomposition, agent delegation, prompt hygiene, iterative loops, and how to review and steer agentic outputs effectively.
Practical experience with agent evals and tracing.
Solid engineering fundamentals. Testing, debugging, performance optimization, and reliability are second nature to you.
You build a strong understanding of our products, workflows, data, and operational constraints quickly.
You ship improvements that make the product smarter, more scalable, and more useful in production.
You apply AI in ways that are practical and durable, not gimmicky.
You become a trusted partner to product and operations by solving messy real-world problems.
You raise the quality of the systems you touch, including code, data models, reliability, and deployment.

Nice-to-haves

Experience in logistics, fulfillment, 3PL, or ecommerce is a plus.
Familiarity with agent tooling ecosystems, including prompt and version stores, eval harnesses, and tracing and telemetry platforms such as OpenTelemetry, LangSmith, Datadog, Honeycomb, or similar, is a plus.
Experience with RAG architectures, including vector databases, indexing strategies, chunking approaches, and retrieval evaluation, is a plus.

Application prep

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Keywords to mirror

aillmpythonreacttypescriptnode.js

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