- Architect and build multi-agent systems that orchestrate the core workflows of 1Source, 1Data, and 1Xcess- RFQ generation, supplier discovery, price benchmarking, BOM parsing, and inventory matching.

- Design agent graphs using frameworks such as LangGraph, CrewAI, or AutoGen, defining agent roles, tool registries, state machines, escalation paths, and human-in-the-loop checkpoints.

- Build and maintain MCP (Model Context Protocol) servers that expose 1Buy.ai 's internal data and business logic as structured, composable tools consumable by AI agents and external LLM clients.

- Define tool schemas, function signatures, and capability registries so that agents across all products can discover and invoke capabilities reliably and safely.

- Implement guardrails, retry logic, fallback strategies, and audit logging for every agentic workflow; production agents must be observable and recoverable.

GenAI Engineering :

- Build RAG pipelines for datasheet extraction, BOM parsing, RFQ drafting, and supplier communication- grounding LLMs in 1Buy.ai 's proprietary component data.

- Design and manage embedding strategies : choose the right embedding models, chunk sizes, retrieval architectures (hybrid dense-sparse search), and re-ranking layers for each use case.

- Fine-tune and adapt open-source LLMs (Llama 3 Mistral, Phi, Qwen, or equivalents) for domain-specific tasks- component classification, part number normalization, and lifecycle prediction from text.

- Build prompt engineering systems that are version-controlled, evaluated, and reproducible not ad-hoc prompts left in notebooks.

- Evaluate and integrate frontier model APIs (OpenAI, Anthropic, Gemini) alongside self-hosted open-source models based on cost, latency, and capability trade-offs.

Classical ML and Predictive Intelligence :

- Build and deploy classical ML models for pricing signal detection, demand forecasting, lead time prediction, lifecycle risk scoring, and inventory age risk- using gradient boosting, time-series models, and clustering techniques.

- Own the full ML lifecycle : feature engineering, model training, offline evaluation, A/B testing, production monitoring, drift detection, and retraining pipelines.

- Make principled decisions on when to use a simple statistical model versus a large LLM, optimizing for cost, latency, and explainability at every layer.

MCP, Tools, and Integrations :

- Design and maintain the MCP server layer that exposes 1Buy.ai 's business capabilities (pricing lookups, supplier scoring, BOM analysis, and RFQ status) as callable tools for internal

agents and external AI clients.

- Define a coherent tool taxonomy across all products- ensuring agents can compose tools from 1Source, 1Data, and 1Xcess without tight coupling or redundancy.

- Build integration connectors for distributor APIs, ERP systems, and data feeds that are agentic-friendly : structured outputs, error contracts, and rate-limit-aware retry logic.

- Stay ahead of the MCP ecosystem : evaluate new servers, contribute open-source tooling where it benefits the platform, and ensure 1Buy.ai 's stack is composable with the broader AI ecosystem.

MLOps, Infrastructure, and Open-Source Stack on AWS :

- Own the end-to-end MLOps stack on AWS : SageMaker for model training and hosting,

- Lambda and ECS for lightweight inference, S3 and RDS for data persistence, and CloudWatch for observability.

- Prefer open-source tooling at every layer : MLflow for experiment tracking, Qdrant or Weaviate for vector search, Airflow or Prefect for pipeline orchestration, Ollama for local inference, and Hugging Face for model management.

- Build containerized inference services (Docker, Kubernetes / EKS) with autoscaling, blue/green deployment, and latency SLAs- every production model must have a runbook.

- Implement model monitoring : track prediction drift, data drift, latency percentiles, and business KPI alignment- automated alerts before humans notice degradation.

- Drive cloud cost discipline : choose self-hosted open-source over paid APIs wherever performance is equivalent; benchmark and document every trade-off.

Vibe Coding and Engineering Culture :

- Actively use and champion AI-assisted development tools- Cursor, GitHub Copilot,

- Windsurf, Bolt, or equivalents- and set the standard for how the team uses them to ship

faster.

- Guide the Vibe Coder in translating product requirements into working AI-assisted prototypes and production-ready components- from idea to demo in hours.

- Evaluate and onboard new vibe coding and AI dev tooling as the ecosystem evolves; what is

state-of-the-art today will be table stakes in six months.

- Create a culture where shipping beats theorizing : every model, every agent, and every pipeline is measured against a business KPI within its first sprint in production.

Company-Wide AI Strategy and Leadership :

- Define the multi-year AI roadmap for 1Buy.ai - identifying where agentic AI, fine-tuned LLMs, and classical ML create the most durable business value across all products.

- Partner with the SVP of Data Products, VP of Sourcing Products, and CPTO to embed AI

capabilities into core product workflows- you are the connective tissue between product

ambition and technical reality.

- Champion responsible AI : define evaluation frameworks, bias checks, and guardrails for all agentic systems before they touch production data.

- Represent AI strategy and technical credibility to investors, enterprise customers, and technology partners when required.

- Recruit, mentor, and develop the ML Engineer and Vibe Coder, setting a high bar for craft, velocity, and continuous learning.

Requirements :

- You are a coder first. Your GitHub is active. You write Python daily. You debug agent traces, read stack traces, and review PRs- you do not delegate engineering to your team and review only outputs.

- 11+ years of progressive experience in software engineering, data science, ML, or AI with a clear arc from hands-on engineering into technical leadership.

- At least 5 years leading or owning production AI/ML systems- not research prototypes. Systems that received real traffic, failed in real ways, and were debugged and improved in production.

- Strong software engineering fundamentals : system design, API design, distributed systems, data structures, and clean code practices. You have built platforms from scratch, not just notebooks.

- Hands-on agentic AI experience : you have built multi-agent systems using LangGraph, CrewAI, AutoGen, or equivalent frameworks- including tool use, state management, and human-in-the-loop design.

- MCP and tool ecosystem fluency : you understand the Model Context Protocol, have built or consumed MCP servers, and can design composable tool taxonomies for multi-agent systems.

- Deep GenAI engineering : RAG pipelines, embedding strategies, prompt versioning, LLM evaluation frameworks, and fine-tuning (LoRA/QLoRA on open-source models).

- Classical ML proficiency : gradient boosting (XGBoost, LightGBM), time-series forecasting, clustering- you know when a simple model outperforms a large LLM and have the discipline to ship it.

- AWS fluency : SageMaker, Lambda, ECS, S3 RDS, CloudWatch, EKS- you can architect, deploy, and operate AI workloads on AWS without needing a dedicated DevOps team.

- Open-source conviction : you default to open-source, benchmark before paying for proprietary services, and contribute back to the ecosystem where it makes sense.

- Vibe coding fluency : you actively use Cursor, Copilot, or equivalent tools to compound your team's velocity and can teach others to do the same.

- Strong communication- able to explain model choices, agent architectures, and trade-offs clearly to the CPTO, product leads, and non-technical stakeholders.

Preferred :

- Experience building AI products in supply chain, procurement, logistics, or industrial B2B

contexts.

- Exposure to electronics OEMs, EMS, semiconductor, or electronics distribution domains, even indirect exposure via data, tooling, or adjacent B2B platforms.

- Experience with multimodal AI- parsing PDFs, images (datasheets, BOMs), and structured/unstructured data in combination.

- Familiarity with Ollama or vLLM-based local inference stacks for cost-efficient, low-latency use cases on EC2 or EKS.

- Contributions to open-source AI or ML projects- libraries, tools, models, or published evaluations.