LockedIn AI
LockedIn AI is the #1 real-time AI interview and meeting copilot, trusted by over one million users worldwide. We are a fast-growing company building the most advanced career preparation platform on the market.
Our platform delivers real-time, AI-powered assistance during live job interviews, coding assessments, and professional meetings — helping candidates communicate with clarity, confidence, and competence.
Role Overview
We’re looking for a resourceful AI Integration Specialist to own the implementation, connection, and optimization of AI-powered systems across LockedIn AI’s product and operational infrastructure.
This is a builder-connector role — you’ll bridge AI technologies and business systems, ensuring that LLMs, speech-to-text engines, and generative AI capabilities are seamlessly embedded into every layer of the platform.
You will own end-to-end integration of AI models with our tech stack, third-party services, and internal tools — making AI capabilities flow reliably through APIs, webhooks, data pipelines, and microservices to deliver a seamless experience for 1M+ users.
The ideal candidate combines strong software engineering fundamentals with deep knowledge of how AI models operate in production. You take a working model and make it talk to everything else — databases, frontends, cloud services, analytics platforms, and external APIs. You move fast, handle edge cases gracefully, and obsess over reliability at scale.
Key Responsibilities
AI Model & Platform Integration
- Design, develop, and deploy production-grade integrations connecting LLM APIs (OpenAI, Anthropic, open-source), speech-to-text systems, and generative AI models with LockedIn AI’s core product and internal tools
- Build model routing logic, fallback mechanisms, and multi-model orchestration pipelines that ensure consistent performance and cost efficiency
- Transform ML model outputs into functional, reliable product features by building connective tissue between AI inference endpoints and application layers
- Integrate AI platforms (OpenAI, Hugging Face, Vertex AI, Amazon SageMaker) into existing workflows with a focus on latency, accuracy, and scalability
API Development & System Connectivity
- Design and build RESTful APIs, GraphQL endpoints, webhooks, and event-driven connectors that enable AI models to communicate with our product, databases, and third-party services
- Develop microservices and middleware that translate between AI model outputs and downstream system formats, with graceful error handling and data integrity guarantees
- Build and maintain integrations with third-party platforms — CRM systems, communication tools, analytics services, and cloud infrastructure
- Ensure all API integrations are well-documented, versioned, and designed for backward compatibility
Data Pipeline Architecture
- Build automated pipelines for ingestion, transformation, enrichment, and delivery that feed AI models and power product analytics
- Design data flows connecting structured and unstructured data sources to AI systems, enabling accurate real-time inference and retrieval-augmented generation (RAG)
- Implement data validation, quality checks, and monitoring at each pipeline stage to catch anomalies before they impact model performance
- Optimize pipelines for throughput and latency so real-time AI features hold up under high user load
Deployment, Monitoring & Reliability
- Deploy AI integrations using containerized environments (Docker, Kubernetes) with CI/CD pipelines tailored to AI/ML workloads
- Build monitoring dashboards and automated alerting for all integration systems — tracking latency, uptime, error rates, and model response quality
- Implement self-healing mechanisms, retry logic, circuit breakers, and graceful degradation to maintain platform reliability at scale
- Troubleshoot integration failures across the stack — from API errors and pipeline breakdowns to inference timeouts and edge-case handling
Cross-Functional Collaboration
- Work with co-founders, product, engineering, and operations to translate business needs into AI integration opportunities
- Map existing workflows, identify manual processes, and architect AI-powered integrations to streamline and automate them
- Document end-to-end integration architectures, data flow diagrams, and runbooks to support team onboarding and knowledge sharing
- Gather feedback from end users, measure integration performance and adoption, and iterate based on real-world data
Security, Privacy & Compliance
- Ensure all AI integrations comply with LockedIn AI’s privacy-first design philosophy, including stealth mode and user data protection
- Implement security best practices across integrations — encrypted data handling, access controls, authentication mechanisms, and audit trails
- Stay current on data privacy regulations and responsible AI practices relevant to AI-assisted career tools
Required Qualifications
Experience
- 3+ years in software development, systems integration, or platform engineering with hands-on experience integrating AI/ML models into production systems
- Demonstrated track record building API-driven integrations, automated data pipelines, and connecting AI inference endpoints with application layers
- Hands-on experience with LLM APIs and platforms (OpenAI, Anthropic, Google AI, Hugging Face, or open-source LLMs) including prompt engineering and model evaluation
- Experience designing and deploying microservices, webhooks, and event-driven architectures in production environments
- Startup or high-growth environment experience preferred — comfortable working in ambiguity and shipping fast
Education
- Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related field.
- Relevant cloud or AI certifications (AWS Solutions Architect, Azure AI Engineer, Google Cloud Professional) are a plus but not required — we value demonstrated skill over credentials.
Technical Stack
- Languages: Python (primary) + one additional backend language — JavaScript/Node.js, Go, or Java
- APIs: REST, GraphQL, webhooks, event-driven systems (Kafka, RabbitMQ, or similar)
- Cloud & infrastructure: AWS, GCP, or Azure; Docker, Kubernetes; CI/CD pipelines
- Data pipelines: Airflow, Prefect, Temporal, or similar orchestration tools
- Databases: SQL and NoSQL; familiarity with ETL/ELT processes and data transformation
- Observability: Datadog, Grafana, Prometheus, or similar monitoring platforms
Soft Skills
- Systems-thinking mindset — you naturally see how components connect and architect integrations that are reliable, scalable, and maintainable
- Strong written and verbal communication — you document integration architectures clearly and explain trade-offs to non-technical stakeholders
- Self-starter — you thrive with autonomy, move fast, and take end-to-end ownership of integration challenges
- Collaborative — you work across engineering, product, data, and operations and translate business needs into technical solutions
Preferred Qualifications
- Experience integrating real-time AI systems, speech-to-text engines (Whisper, Deepgram), or streaming audio processing into production
- Background building retrieval-augmented generation (RAG) systems or knowledge-grounded AI integrations
- Experience with agentic AI workflows, multi-tool orchestration, or MCP server integration patterns
- Familiarity with RPA tools (UiPath, Power Automate) or code-based process automation
- Experience in career tech, edtech, or SaaS B2C space
- Contributions to open-source integration frameworks, API tooling, or AI/ML projects
- Prior startup founding or early employee experience (Seed to Series A stage)
To apply for this job please visit www.lockedinai.com.