AI Developer

TEVET

67d•Hybrid

About The Position

We seek an AI Solutions/Developer to design and deploy AI-driven solutions that streamline business processes across customer service, operations, and compliance. You will apply NLP and machine learning to automatically categorize and route inbound inquiries, extract data from documents like RFQs and POs, generate proposal documents with generative AI, and perform AI-based compliance reviews. The role involves full-cycle development of AI models (from prototyping to production), close collaboration with cross-functional teams, and ensuring the scalability, accuracy, and ethical use of AI within the organization. Experience in enterprise automation or AI startups is highly valued, as our environment is fast-paced and focused on delivering measurable efficiency gains for the business. As an AI Developer at TEVET, you will design and deploy intelligent automation solutions that integrate our AI models with enterprise platforms like NetSuite (ERP), HubSpot (CRM), and Microsoft 365. You’ll build and extend custom plugins and connectors – leveraging tools such as Microsoft Copilot Studio and OpenAI APIs (e.g. ChatGPT) – to streamline workflows and boost productivity across the organization. This role bridges cutting-edge AI with core business applications: automating data flows between systems, embedding AI-driven processes into CRM/ERP modules, and adding generative AI features into everyday tools like our Office 365 suite. The ideal candidate has hands-on experience developing AI integrations and automations within ERP/CRM ecosystems, and is comfortable building solutions like Power Automate scripts, CRM extensions, or chatbots that enhance efficiency while working seamlessly with our existing software stack

Requirements

Bachelors’ degree in Computer Science, Information Systems, Information Technology, Engineering, or related field.
5+ years of software development experience (with at least 2+ years in AI/ML engineering), building production-grade solutions.
Programming & ML Expertise: Strong programming skills in Python (writing clean, efficient code using OOP and proper design patterns) and familiarity with software engineering best practices (version control, code reviews, CI/CD). Extensive Experience using JavaScript or TypeScript where needed for frontend automation or CRM extension. Familiarity with cloud-based AI deployment and integration.
Document Processing: Experience processing unstructured text and PDFs. Knowledge of OCR tools (such as PyMuPDF, Tesseract) and use of regex or rule-based parsing to extract data from forms, tables, and documents. Proficiency in REST API design and authentication (OAuth, API keys). Strong foundation in data serialization and validation (Pydantic, JSON schema).
API & Integration: Strong proficiency developing and consuming REST APIs to connect AI services with internal systems. Experience building custom connectors or plugins to extend capabilities of platforms like HubSpot or NetSuite.
Cloud & DevOps: Familiarity with deploying AI solutions on cloud platforms. Experience deploying on Azure Functions, Container Apps, or App Service. Working knowledge of Azure ecosystem: Key Vault, Cognitive Search, Blob Storage, Application Insights.
Generative AI Knowledge: Strong understanding of large language models (LLMs) and generative AI. Ability to fine-tune or prompt-engineer models like GPT for enterprise use-cases and implement retrieval-augmented generation or similar techniques when appropriate. Familiarity with prompt engineering and structured output validation. Deep familiarity with OpenAI / Azure OpenAI APIs (GPT-4, GPT-4o, embeddings, function-calling/JSON mode).
Analytical Mindset: Strong grasp of machine learning fundamentals, data structures, and algorithms. Excellent problem-solving skills with the ability to translate business problems into AI solutions and interpret model results in business terms.
AI Integration Expertise: Proven experience building AI-powered automations or extensions within enterprise ecosystems such as NetSuite (ERP), HubSpot (CRM), and Microsoft 365.
Generative AI & LLMs: Hands-on experience working with ChatGPT/OpenAI APIs, other large language models (LLMs), or comparable services. Ability to implement prompt engineering, build contextual pipelines (e.g. RAG), and safely integrate generative AI into business workflows. Solid understanding of RAG architecture (vector embeddings, document chunking, retrieval).
Copilot & Automation Frameworks: Familiarity with Microsoft Copilot and Copilot Studio (or similar tools like Power Automate, Logic Apps) for creating low-code/no-code AI agents, automations, or workflow integrations.
Enterprise Data Fluency: Comfortable working with structured and semi-structured data from ERP/CRM systems. Understands business objects like quotes, orders, invoices, tickets, and customer profiles, and how they relate across systems. Comfort with document automation (python-docx, python-pptx, PyPDF2, OCR pipelines).
Security, Privacy, and Compliance: Strong understanding of enterprise AI governance, secure data handling, and how to design automations that respect data privacy, access controls, and auditability—particularly when integrating AI into regulated processes (e.g. compliance reviews).
Collaboration & Communication: Ability to collaborate with operations, compliance, and IT stakeholders to scope problems and build automation solutions that deliver business value—no ivory tower model builders.

Nice To Haves

Advanced Degree: Master’s or PhD in Computer Science, Data Science, or a related field, which demonstrates advanced knowledge of AI/ML (or equivalent research experience)
Experience with LangChain, LlamaIndex, or Semantic Kernel
Prior work on vision/LLM hybrid extraction (e.g., GPT-4o, Claude Opus)
RPA/Workflow Tools: Exposure to Robotic Process Automation tools or BPM software (e.g., UiPath, Automation Anywhere, Blue Prism) and understanding of how to integrate AI with automated workflows. Prior work integrating with NetSuite Suitelets/RESTlets or HubSpot custom workflows.
MLOps & Deployment: Experience with MLOps practices and tools such as MLflow, Kubeflow, or Airflow for managing the ML lifecycle (from training to monitoring). Familiarity with CI/CD pipelines for model deployment and infrastructure-as-code for AI services
Domain Experience: Background in industries like finance, logistics, or compliance-heavy sectors. Experience applying AI solutions in domains such as regulatory compliance, supply chain, or customer service can be a plus (e.g., understanding industry-specific data and requirements) Exposure to FAR/DFARS or regulated document workflows.
Contributions & Leadership: Demonstrated contribution to the AI/ML community – such as open-source projects, research publications, or patents in relevant fields
Leadership or mentorship experience in AI projects and staying up to date with the latest AI research trends.
Leverage AI tools and resources to enhance productivity, streamline workflows, and improve the quality and efficiency of task completion, while ensuring accuracy, ethical use, and compliance with organizational standards.

Responsibilities

Design and implement LLM orchestration services (Python FastAPI or Azure Functions preferred) that handle structured prompts, schema validation, and model responses.
Integrate Azure OpenAI with our business systems (NetSuite, HubSpot, SharePoint, internal APIs).
Implement retrieval-augmented generation (RAG) pipelines using Azure Cognitive Search over SharePoint documents.
Build document processing flows for extracting structured data from PDFs and generating editable proposals (DOCX/PPTX).
Design classification models to categorize inbound emails or other messages by content, returning normalized JSON for use in HubSpot workflows.
Handle model governance: implement retry, throttling, cost logging, and human-in-the-loop review logic.
Collaborate with internal HubSpot and NetSuite developers to expose clean REST/HTTPS endpoints that they can call.
Own security and deployment of LLM services within Azure: Key Vault, Function Apps, Container Apps, and Cognitive Search.
Benchmark and evaluate model performance (accuracy, latency, cost) and iterate on prompts and retrieval logic.
Drive AI initiatives that deliver quantifiable ROI and measurable efficiency gains across operations, ensuring every process improvement translates to tangible business impact.
Automate Customer Communications: Develop and deploy AI models to automatically classify and route incoming customer emails and inquiries to the right teams, improving response times and consistency
Document Data Extraction: Design AI/machine learning pipelines to extract and process data from unstructured documents (e.g. RFQs, POs, invoices, e-mails etc.), eliminating manual data entry and reducing errors
Proposal Generation: Leverage generative AI (LLMs) to auto-generate proposal drafts and templated documents, accelerating the sales and RFP response process while maintaining tailored content
Compliance & Quality Review: Build AI-driven tools to analyze documents for compliance with policies and checklists, flagging issues and ensuring regulatory requirements are met with less manual effort
Cross-Team Collaboration: Work with stakeholders in IT, operations, sales, and compliance to identify automation opportunities and integrate AI solutions into existing workflows and systems
Model Monitoring & Improvement: Monitor the performance of deployed AI models and agents, analyze error rates and feedback, and retrain or tune models for continuous improvement in accuracy and efficiency
Documentation & Best Practices: Maintain thorough documentation of AI workflows, model architectures, and decision logic. Uphold best practices in coding, testing, and AI ethics to ensure solutions are transparent and auditable