The Lead/Staff Engineer - Applied AI role at HighLevel is a remote position based in India, focused on the company's AI & Marketplace division. The role involves high-level engineering leadership to drive applied AI initiatives. Responsibilities include designing and implementing AI-driven solutions to enhance the platform's capabilities. Candidates are expected to have significant experience in engineering leadership and applied AI technologies. The application process requires submission of a resume, contact details, current compensation, notice period, and work authorization status for India. HighLevel utilizes AI tools during the recruitment process to assist in reviewing applications and analyzing resumes, though final hiring decisions remain human-led.
For full details about this role, click “Apply on company site” to view the employer’s original posting.
Skills for this role
Your skill match
Checking your profile…
YOUR NEXT STEP
Get interview-ready for this role
A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
Engineering Leadership in Applied AI
The role requires driving AI initiatives within the AI & Marketplace division. You must demonstrate the ability to lead technical strategy and mentor teams while translating complex AI research into functional, scalable platform features.
Design and Implementation of AI-Driven Solutions
A core priority is the end-to-end development of AI capabilities. You will be expected to architect systems that integrate AI models into the existing HighLevel platform, ensuring these solutions are robust, performant, and directly enhance user value.
Likely challenges to think through
Balancing Innovation with Platform Stability
As a Lead/Staff Engineer, you will face the challenge of introducing cutting-edge AI technologies into a production environment without disrupting existing platform functionality. Prepare by discussing your methodology for phased rollouts, A/B testing, and monitoring AI model performance in live systems.
Navigating Ambiguity in AI Product Development
Applied AI roles often involve high levels of uncertainty regarding model efficacy and data requirements. Prepare by highlighting your experience in defining clear technical milestones and managing stakeholder expectations when AI outcomes are non-deterministic.
03
Practice your answers
Technical practice
Architecting Scalable AI Solutions
What to coverDiscuss your approach to designing AI-driven features that integrate into a large-scale platform. Focus on trade-offs between latency, cost, and model accuracy when deploying generative AI at scale.
Applied AI Lifecycle Management
What to coverExplain your process for moving an AI model from prototype to production. Address key considerations like monitoring, drift detection, and continuous improvement loops for deployed models.
Marketplace Integration Strategies
What to coverDescribe how you would design an AI-powered marketplace feature that balances user experience with technical constraints. Suggest how to handle multi-tenant data isolation and performance requirements.
Evaluating Generative AI Performance
What to coverDetail the metrics and evaluation frameworks you use to assess the quality of generative AI outputs. Discuss how you handle non-deterministic results in a production environment.
Behavioural practice
Engineering Leadership in AI Initiatives
What to coverProvide an example of a time you led a technical team through a complex AI project. Focus on how you aligned technical strategy with business goals and managed stakeholder expectations.
Navigating Technical Ambiguity
What to coverDescribe a situation where you had to define the technical direction for an ambiguous AI problem. Explain how you evaluated potential paths and communicated your decision to the team.
Cross-Functional Collaboration
What to coverShare an experience where you collaborated with non-technical stakeholders to implement an AI solution. Highlight how you translated technical limitations into actionable business insights.
Mentorship and Technical Growth
What to coverGive an example of how you have mentored junior or mid-level engineers in the AI space. Discuss how you foster a culture of technical excellence and continuous learning within your team.
02
Sharpen your knowledge
Topics to prepare
Applied AI Architecture
Review architectural patterns for integrating AI models into existing SaaS platforms. Exercise: Design a high-level system diagram showing how an AI service would interact with a marketplace ecosystem to enhance user features, focusing on latency and scalability.
Engineering Leadership in AI
Focus on the responsibilities of a Lead/Staff level engineer in driving technical strategy. Exercise: Prepare a case study from your past experience where you led a cross-functional team to deliver a complex technical project, specifically highlighting how you managed technical debt while implementing new AI capabilities.
Skills to revise
AI Solution Implementation
Practical application of AI technologies to solve business problems. Exercise: Select a specific AI use case relevant to a marketing or CRM platform and outline the end-to-end implementation plan, including model selection, data pipeline requirements, and evaluation metrics.
Technical Strategy Communication
Articulating complex AI initiatives to stakeholders. Exercise: Draft a technical proposal or whitepaper for a hypothetical AI-driven feature, ensuring the value proposition and technical feasibility are clear for both engineering and product leadership.
04
Your preparation checklist
Prepare a portfolio of applied AI projects
Curate 2-3 case studies demonstrating your experience in designing and implementing AI-driven solutions. Focus on the technical architecture, the specific AI models utilized, and the measurable impact these solutions had on platform capabilities or business outcomes.
Develop an engineering leadership narrative
Draft specific examples of your experience leading engineering teams in an AI context. Be prepared to discuss how you mentor developers, manage technical debt in AI projects, and drive high-level strategic initiatives within a remote-first environment.
Optimize your resume for AI-assisted screening
Since HighLevel uses AI tools to analyze resumes, ensure your document clearly highlights keywords related to Generative AI, system architecture, and engineering leadership. Use a clean, standard format that allows for easy parsing of your technical skills and work history.
Formulate strategic questions for the interview
Prepare questions regarding the current AI & Marketplace division roadmap, the specific technical challenges the team is facing, and how the company balances rapid AI innovation with platform stability and reliability.
Verify administrative and compliance details
Ensure you have your current compensation, notice period, and India work authorization status clearly documented. Being prepared with these details allows you to complete the application process efficiently and accurately as required by the submission form.
Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
Share this opportunityKnow someone who would be a great fit?