Senior Full Stack Engineer (Conversational AI and Voice Applications)

Accountabilities

You will take ownership of the technical approach and delivery of a working conversational AI solution, balancing product requirements, implementation feasibility, reliability, security, and operating costs.

• Technical Assessment & Architecture

  • Assess existing LMS capabilities, reusable prototypes, and potential platform extensions before recommending a configured or custom solution.

  • Document feasibility, dependencies, delivery estimates, technical risks, and operating-cost assumptions.

  • Define the application architecture and implementation approach based on business needs and available resources.

  • Voice Application & Conversational AI Development

    • Build a functional two-way voice prototype supporting responsive audio, natural turn-taking, interruptions, session state, and recovery from common failures.

    • Develop an AI customer persona with approved facts, objections, and objectives, ensuring consistent behavior across different learner responses.

    • Design natural conversation endings without revealing expected answers or guiding learners toward predetermined responses.

    • Integrate speech and language model services using a real-time speech-to-speech approach or a combination of speech recognition, LLM, and speech synthesis.

    • Convert approved training materials into configurable, versioned scenarios and assessment rubrics, keeping the customer simulation separate from post-session evaluation.

    • LMS Integration & Learner Experience

      • Investigate integration options with Absorb LMS, including learner identity, browser microphone permissions, and application launch workflows.

      • Validate embedded access and a separate-tab fallback, then implement the agreed authentication and completion approach.

      • Ensure the application works across agreed browsers and LMS launch paths.

      • Assessment, Reporting & Data Management

        • Implement evidence-based scoring and balanced coaching using approved weights, exclusions, and serious-error rules.

        • Calculate scores programmatically and ensure generated coaching PDFs accurately reflect the underlying assessment results.

        • Store learner, agency, scenario, rubric, and attempt records in a structured database.

        • Generate branded PDF reports and agreed data exports while preserving information for future reporting integrations.

        • Distinguish actual audio-based measurements from estimates derived from text, including optional speaking metrics only when validated and agreed.

        • Usage Management, Security & Reliability

          • Implement agency and learner usage allowances, reset rules, alerts, and limits.

          • Handle concurrent requests, retries, and failed attempts without duplicate charges or assessment results.

          • Protect agency data boundaries, credentials, and recordings through appropriate access controls, retention, and deletion policies.

          • Maintain useful application logs while avoiding unnecessary exposure of personal information.

          • Deployment, Testing & Handover

            • Deploy and maintain the application, manage environments and secrets, monitor latency and usage, and resolve technical defects.

            • Conduct automated testing and debugging across browser, backend, and AI services.

            • Demonstrate progress weekly, maintain an agreed task tracker, and proactively communicate blockers and technical trade-offs.

            • Deliver source code, configuration, deployment instructions, monitoring guidance, cost assumptions, and operational documentation.

            • Support user testing, scoring calibration with content specialists, defect resolution, and final handover.

            Requirements

            The ideal candidate is a hands-on full stack engineer with proven experience delivering conversational or voice-based applications, strong frontend and backend development skills, and the ability to integrate AI services into reliable production-ready products.

            • Typically 5–8 years of software engineering experience, with relevant delivery experience taking priority over tenure.

            • Proven ownership of at least one working two-way voice application or conversational product involving spoken interaction. You should be able to explain your contribution, architecture, latency considerations, interruption handling, failure recovery, and usage costs.

            • Strong frontend development skills with TypeScript and React or a comparable framework.

            • Practical experience with browser audio capture and playback, asynchronous UI behavior, microphone permissions, and session error handling.

            • Backend development experience using Python (such as FastAPI) or Node.js with TypeScript.

            • Familiarity with REST APIs, WebSockets and/or WebRTC, authentication, background processing, and relational data modeling.

            • Practical experience integrating LLM and speech services through real-time speech-to-speech or a combination of speech recognition, language models, and speech synthesis.

            • Strong prompt and context design skills, including structured model outputs, scenario grounding, and evaluation against approved facts.

            • Ability to recognize when an AI system should acknowledge missing information rather than generate unsupported responses.

            • Experience with third-party API integrations, secure session management, SQL databases such as PostgreSQL, PDF generation, and reliable data export workflows.

            • Familiarity with automated testing, debugging, Git, code reviews, deployment pipelines, Docker or equivalent packaging, and at least one cloud environment.

            • Clear written English and the ability to translate business requirements into small, testable releases while explaining assumptions and trade-offs to non-technical stakeholders.

            • A degree in a relevant discipline or equivalent practical experience.

            • Near-term availability is preferred.

            Preferred experience includes:

            • Absorb LMS or another learning management system.

            • LTI 1.3, OIDC/SSO, or secure external-tool launch integrations.

            • Multi-tenant SaaS architecture and agency-level data separation.

            • Rubric-based AI evaluation, training, or coaching products.

            • Insurance workflows and document retrieval solutions.

            • Integrations with Google Sheets, BigQuery, HubSpot, or Zapier.

            • Avatar integration, if introduced in a later project phase.

            You do not need experience with every platform listed; strong expertise in one coherent technical stack is sufficient.

            Benefits

            • Fully remote engagement within India, with Bengaluru preferred.

            • Initial paid technical assessment lasting 1–2 weeks.

            • Planned full-time engagement of approximately 35–40 hours per week over an initial 8–12-week period.

            • Opportunity to lead the development of a practical conversational AI and voice-based training solution.

            • Potential for continued part-time support for 3–6 months after the initial engagement, with scope and hours agreed separately.

            • Initial planning allowance for ongoing support of approximately 10–20 hours per month.

            • Opportunity to work with international stakehol