ItsaCheckmate

Product Manager

• Translate product vision into clear, actionable roadmaps and delivery plans for Voice AI solutions in the restaurant industry.
• Gather and prioritize requirements from customers, internal stakeholders, and market research.
• Use data insights, user feedback, and industry trends to identify opportunities and define impactful product features.
• Define, scope, and launch features that improve AI accuracy, expand coverage, and enhance operational performance.
• Set KPIs and success metrics, monitor performance, and adjust priorities based on data insights.
• Rapidly prototype and build proof-of-concepts to validate ideas before full-scale development.
• Work with engineering and data science to test and iterate on conversational AI capabilities.
• Conduct competitive analysis and stay ahead of trends in LLMs, speech recognition, and conversational AI.
• Manage timelines, dependencies, and trade-offs to ensure successful product launches.
• Continuously refine the product based on performance metrics and customer feedback.
• Take true ownership of the product, and drive to not just implementation but beyond.

Principal Product Manager – Voice AI

Data Scientist

• Own end-to-end product experimentation: hypothesis generation, metric definition, experimental design (A/B, multivariate, sequential testing), analysis, and executive-level interpretation.
• Design and maintain ML-powered evaluation frameworks for product changes, automation quality, and system reliability (e.g., order accuracy, routing, error rates, conversion).
• Build and deploy predictive models, classifiers, and ranking systems that power experimentation, personalization, and product optimization.
• Partner with product and engineering to test new features, workflows, and ML models through controlled experiments and incremental rollouts.
• Lead offline and online model evaluation, comparing baselines, candidate models, and product variants using rigorous statistical methods.
• Use causal inference and quasi-experimental methods when randomized experiments are not feasible.
• Develop experiment pipelines and instrumentation: logging, dashboards, monitoring, and automated analysis to ensure measurement integrity.
• Perform failure-mode and error analysis to guide product iteration and model improvement.
• Translate experiment outcomes into clear product decisions, influencing roadmap prioritization and system design.
• Drive experimentation at scale in a fast-moving environment, balancing speed, rigor, and business impact.
• Lead and mentor data scientists and analysts, setting standards for experimentation, modeling, and evaluation across the organization.

Head of Data Science – Product Experimentation, Machine Learning

• Design, test, and optimize LLM prompts for conversational AI, text classification, and structured data extraction tasks.
• Build evaluation pipelines to analyze prompt performance using quantitative metrics, human-in-the-loop feedback, and business KPIs.
• Conduct prompt experiments and regression testing to ensure stability, accuracy, and safety as models evolve.
• Collaborate with Machine Learning, Product, and Operations teams to translate business objectives into scalable, data-driven prompt-engineering strategies that enhance model accuracy, efficiency, and real-world usability.
• Use Python/SQL to analyze model outputs, identify anomalies, and automate quality checks.
• Document best practices and contribute to internal frameworks for prompt evaluation and continuous improvement.
• Communicate findings effectively to technical and non-technical stakeholders, driving measurable business impact through insight-driven decisions.

Senior Prompt Engineer – Data Science, Quality Analysis

Backend Engineer

• API Development & Integration: Design and build new REST API endpoints using Flask and the OpenAPI specification. You will also manage complex integrations with various external and internal third-party APIs (e.g., payments, loyalty, other services).
• Performance Optimization: Proactively identify and resolve performance bottlenecks. You'll use tools like Datadog APM, advanced SQL query optimization, indexing, and caching (e.g., Redis) to ensure a low-latency, high-performance end-user experience.
• Database & Core Logic: Work directly with MySQL without an ORM, writing and optimizing complex, custom SQL queries to interact with a database featuring hundreds of tables.
• System Modernization & Quality: Refactor and modernize parts of a large, 10-year-old codebase. You will also take ownership of expanding our automated test suite to ensure robustness and quality.
• Problem Solving: Track down and resolve complex bugs across the application.
• Feature Expansion: Build new backend functionalities that directly expand the feature set of our consumer-facing products.

Senior Python Developer

SDET

• Design and develop automation frameworks for:
• Web using Playwright (TypeScript/JavaScript)
• React Native Android & iOS apps using Appium with WebdriverIO [TS/JS]
• Build scalable, maintainable automation architecture with reusable utilities, components, and reporting.
• Develop robust automated test suites for regression, smoke, and end-to-end validation.
• Execute tests across virtual and real devices using BrowserStack and AWS Device Farm.
• Integrate automation into CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI, etc.).
• Diagnose and resolve automation failures, flaky tests, and environment issues.
• Collaborate with developers, QA analysts, and product managers to define testability requirements.
• Participate in requirement reviews, feature grooming, and design discussions.
• Maintain automation documentation, best practices, and coding standards.
• Communicate clearly with cross-functional teams and ensure transparency on automation progress and risks.

Senior Software Development Engineer in Test, India

• Architect, build, and maintain Python based backend services that support real-time Voice AI workflows across production environments.
• Collaborate with Machine Learning engineers to integrate LLM and GenAI driven components into scalable application pipelines.
• Develop robust APIs, internal service interfaces, and orchestration layers that support high throughput drive-thru and retail operations.
• Implement and maintain CI and CD pipelines to ensure reliable, automated delivery of backend systems and AI powered services.
• Optimize system performance, reliability, and fault tolerance across distributed environments running on AWS, GCP, or Azure.
• Contribute to model testing, monitoring, and validation workflows that ensure platform stability under real-world load.
• Build prototypes and internal tooling that accelerate experimentation, data analysis, and platform iteration.
• Participate in code reviews, design discussions, and cross-functional planning to ensure engineering quality and alignment with product goals.
• Candidates must be flexible and work during US hours at least till 5 pm EST, which is essential for this role.

Senior Python Engineer – Voice AI

Data Engineer

• Act as the transition point between Prompt Engineering and Data Labeling, translating model and product requirements into concrete data and annotation workflows.
• Design, implement, and maintain scalable data workflows for dataset generation, curation, and ongoing maintenance.
• Ensure data quality and consistency across labeling projects, with a focus on operational reliability for production AI systems.
• Create, review, and maintain high-quality annotations across multiple modalities, including text, audio, conversational transcripts, and structured datasets.
• Identify labeling inconsistencies, data errors, and edge cases; propose and enforce corrective actions and improvements to annotation standards.
• Utilize platforms such as Labelbox, Label Studio, or Langfuse to manage large-scale labeling workflows and enforce consistent task execution.
• Use Python and SQL for data extraction, validation, transformation, and workflow automation across labeling pipelines.
• Leverage LLMs (e.g., GPT-4, Claude, Gemini) for prompt-based quality checks, automated review, and data validation of annotation outputs.
• Implement automated QA checks and anomaly-detection mechanisms to scale quality assurance for large datasets.
• Analyze annotation performance metrics and quality trends to surface actionable insights that improve labeling workflows and overall data accuracy.
• Apply statistical analysis to detect data anomalies, annotation bias, and quality issues, and partner with stakeholders to mitigate them.
• Collaborate with ML and Operations teams to refine labeling guidelines and enhance instructions based on observed patterns and error modes.
• Work closely with Prompt Engineering, Data Labeling, and ML teams to ensure that data operations align with model requirements and product goals.
• Document data standards, annotation guidelines, and workflow best practices for use by internal teams and external labeling partners.

Data Architect – Annotation

Cloud Engineer

• Define AWS standards and guide platform architecture.
• Lead initiatives that improve scalability, security, performance, and cost efficiency.
• Partner with application engineers to ensure systems are production-ready, observable, and resilient.
• Mentor other engineers to raise cloud maturity of the organization.
• Lead design and evolution of AWS infrastructure.
• Define architectural standards and best practices across AWS services.
• Drive cloud modernization initiatives towards well-architected AWS solutions.
• Own and maintain infrastructure as code using Terraform and/or CloudFormation.
• Build automation for provisioning, configuration, and lifecycle management of AWS resources.
• Define and own monitoring, logging, and alerting standards across AWS.
• Lead infrastructure-related incident response to drive reliability improvements.
• Optimize AWS database services and ensure databases are performant and scalable.
• Implement AWS security best practices and monitor costs efficiently.
• Act as a technical leader and mentor for engineers across teams.

Lead Cloud Engineer

• Design and develop automation frameworks for:
• - Web using Playwright (TypeScript/JavaScript)
• - React Native Android & iOS apps using Appium with WebdriverIO [TS/JS]
• Build scalable, maintainable automation architecture with reusable utilities, components, and reporting.
• Develop robust automated test suites for regression, smoke, and end-to-end validation.
• Execute tests across virtual and real devices using BrowserStack and AWS Device Farm.
• Integrate automation into CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI, etc.).
• Diagnose and resolve automation failures, flaky tests, and environment issues.
• Collaborate with developers, QA analysts, and product managers to define testability requirements.
• Participate in requirement reviews, feature grooming, and design discussions.
• Maintain automation documentation, best practices, and coding standards.
• Communicate clearly with cross-functional teams and ensure transparency on automation progress and risks.