Technical Customer Implementation

About us

EasyBee AI
 is a pioneering force in leveraging artificial intelligence to transform business operations across sectors, enhancing productivity and streamlining processes. Our platform, rooted in generative AI, offers tailored automation solutions, mirroring the efficient dynamics of a beehive. With a world-class team experienced in founding startups, making angel & VC investments, winning patents, steering $100+ million in AI investments at large corporations, and leading digital initiatives globally, we're poised for significant growth. We're committed to innovation, trust, efficiency, and providing a suite of AI agents designed to automate, personalize, and revolutionize business operations.

Curious about AI or ChatGPT?
Think of EasyBee as your business's own team of smart helpers, working tirelessly behind the scenes. Each 'EasyBee' agent, a specialist in its own niche, intelligently automates and streamlines tasks — ensuring coverage and insights gathering across different business needs.

If you are passionate about Generative AI, enjoy working in a fast-paced environment, and are excited about contributing to the growth of an ambitious startup, this opportunity is for you.

About the Role

As a
Technical Customer Implementation & Success Engineer
, you will be the primary
technical architect
and
trusted advisor
for the customer journey post-sale. Your core responsibility is to own the end-to-end technical implementation of
EasyBee AI agents
, ensuring seamless integration and optimal performance, meeting customer's business objectives. You will have outstanding oral & written communication skills across various levels at a customer organization (business, technical, AI adoption stakeholders, leaders/executives).

This high-impact role requires deep technical proficiency to translate complex business requirements into robust, scalable solutions. You will be hands-on in designing, configuring, and validating
ETL pipelines
to efficiently ingest and transform diverse customer data for AI agent training and operation. You will lead and validate efforts in API & MCP integrations across various industry-leading solutions in CRM, FMS (Facilities Management Systems), ERP's, etc. You will conduct rigorous
User Acceptance Testing (UAT)
and performance benchmarking, constantly identifying opportunities for
performance improvements
and architectural optimizations to maximize customer value and system reliability.

Post-deployment, you will drive technical customer success, monitoring agent performance, troubleshooting complex integration issues, and guiding customers on feature adoption and best practices. Your insights will also be crucial in collaborating with Engineering and Product teams, translating field-level technical challenges and successes into actionable product improvements and compelling, technically-detailed case studies and success stories.

Pay & Compensation
: Even though we can't give full salaries, here is our Compensation Plan

Key Responsibilities1. Customer Onboarding & Implementation

• Develop a
  structured onboarding plan
  , setting expectations, milestones, and timelines for implementation.
• Technical Discovery and Solution Architecture:
  Lead kickoff calls to gather business requirements, align stakeholders on project scope, and conduct technical discovery sessions to define the optimal AI agent architecture, including API endpoints, security protocols as applicable (e.g., OAuth 2.0, SAML), and knowledge sessions to explain concepts is a simple, easy-to-understand format.
• ETL Pipeline Design and Data Ingestion:
  Design, implement, and validate robust
  ETL pipelines
  (Extract, Transform, Load) using tools like Python, SQL, or specialized data integration platforms to cleanse, normalize, and ingest customer source data for agent training and operation.
• System Integration and Deployment:
  Execute the hands-on configuration and deployment of the AI agent solution, ensuring seamless bidirectional integration with core customer systems (e.g., CRM, ERP, FMS (Facilities Management System, internal data lakes) via managed SDKs, API, and/or MCP development.
• Initial Performance Baseline:
  Establish and document the initial performance baseline metrics (e.g., latency, throughput, error rates) for the deployed solution to serve as a benchmark for all subsequent optimization efforts.
• Work cross-functionally with engineering, product, and business teams
  to translate customer needs into implementation steps.
• Track progress in
  Notion and Slack
  , ensuring accountability and transparency for both internal teams and customers.

2. Customer Success & Adoption

• Proactive Technical Health Monitoring:
  Implement and manage/utilize technical dashboards to proactively monitor the operational health, resource utilization, and key performance indicators (KPIs) of deployed AI agents.
• Technical Troubleshooting and Escalation:
  Serve as the final technical escalation point for complex integration failures, data sync issues, or agent performance degradation, providing root cause analysis and implementing immediate corrective measures.
• Feature Adoption and Technical Guidance:
  Act as a
  trusted advisor
  , conducting technical workshops and training for customer teams on advanced features, data schema changes, and self-service management tools to maximize long-term solution adoption.
• Own
  post-implementation customer engagement
  , ensuring smooth transitions from deployment to day-to-day use.
• Conduct
  regular check-ins, success reviews, and feedback sessions
  to ensure customers maximize the value of EasyBee AI.
• Serve as an industry expert, proactively identifying opportunities for
  upselling or feature expansion
  .
• Develop and maintain
  customer playbooks, FAQs, and knowledge bases
  to streamline the customer success process.

3. UAT (User Acceptance Testing) & Quality Assurance

• UAT Environment Setup and Data Masking:
  Lead the provisioning and configuration of a dedicated staging/UAT environment, ensuring production parity and strict adherence to data governance policies
• Test Case Development and Execution:
  Develop comprehensive, technically-focused
  test cases
  and scripts that validate system-level requirements, including load testing, data integrity checks, and specific AI agent response accuracy.
• Defect Triage and Resolution Management:
  Systematically track, triage, and manage the resolution of technical defects identified during UAT, coordinating directly with the internal Engineering team to prioritize and verify fixes.
• Ensure customer feedback is
  systematically documented and fed back into the product team
  for continuous improvement.

4. Continuous Improvement & Process Optimization

• Performance Optimization and Tuning:
  Routinely analyze telemetry and log data to identify system bottlenecks in the ETL process or agent inference, implementing configuration changes, infrastructure scaling, or code refactoring for
  performance improvements
  .
• Standardization and Documentation:
  Develop and maintain high-quality, reusable technical artifacts, including deployment scripts, integration blueprints, runbooks, and detailed technical documentation (Notion/wiki) to standardize future implementations.
• Feedback Loop to Product Engineering:
  Systematically aggregate field-level technical feedback, common implementation pain points, and customer requests, translating them into clear, actionable requirements for the Product and Core Engineering teams.

Additionally, you will collaborate cross-functionally to craft messaging that drives demand. Your efforts will include developing case studies and customer success stories.

What Makes You a Great Fit

A successful candidate for this role needs to be a strong hybrid—a
deeply technical architect
who is also a
polished, customer-facing consultant
and a
strategic product advocate
.

Technical Leadership

• Proven expertise in
  enterprise-grade integrations
  (APIs, webhooks, SDKs) across CRMs (Salesforce/HubSpot), ERPs, and FMS platforms.
• Strong understanding of
  secure authentication protocols
  including
  OAuth2.0
  and
  SAML
  .
• Hands-on
  data engineering skills
  : Python for pipeline automation, SQL for data transformation, and experience preparing data for
  AI/ML
  workflows (cleansing, normalization, feature engineering).
• Acts as the
  final technical escalation point
  , capable of deep
  root cause analysis
  across distributed and integrated systems.
• Experience in
  performance optimization
  , including setting baselines, analyzing telemetry/logs, and tuning infrastructure for scale and latency.
• Leads
  UAT setup
  and ensures
  production-environment fidelity
  , including creation of structured QA test cases and data integrity checks.

Customer Ownership & Communication

• Exceptional ability to translate complex technical systems into clear language for
  executives, business users, and engineers
  .
• Demonstrated success
  running end-to-end onboarding/implementation projects
  with milestone ownership and stakeholder alignment.
• Proactively ensures customer success through
  health monitoring dashboards
  , usage insights, and structured feature adoption plans.
• Strong
  business acumen
  , turning customer objectives into precise, scalable technical solutions.

Product Collaboration & Continuous Improvement

• Systematically captures
  field insights
  , pain points, and recurring requests to influence roadmap and platform improvements.
• Creates high-quality
  runbooks, integration playbooks, deployment guides, and reusable scripts
  to standardize implementation delivery.
• Capable of co-authoring
  technical case studies and success narratives
  , showing measurable business outcomes.

Success Metrics

100% of new customers successfully onboarded within agreed timelines

Reduction in implementation cycle time
through process optimizations

High UAT pass rate
with minimal post-deployment issues

Increased customer retention and satisfaction (NPS of 8.0+)

Upsell and expansion opportunities identified from post-implementation engagement