Codvo.ai

• Develop and execute strategic sales plans to achieve revenue and growth targets within the MedTech industry. • Identify and pursue new business opportunities across hospitals, healthcare providers, medical device companies, diagnostics firms, and digital health organizations. • Build and maintain strong relationships with CXOs, procurement teams, clinical stakeholders, and decision-makers. • Lead end-to-end enterprise sales cycles including prospecting, solution pitching, negotiation, contracting, and closure. • Collaborate with product, marketing, and delivery teams to align customer requirements with business solutions. • Drive account expansion strategies and ensure high customer satisfaction and retention. • Monitor market trends, competitor activities, healthcare regulations, and emerging technologies within the MedTech ecosystem. • Prepare sales forecasts, pipeline reports, and business performance metrics for leadership review. • Mentor and manage sales managers, account executives, and business development teams. • Participate in industry conferences, healthcare events, and networking forums to strengthen brand visibility.

• Own end-to-end delivery of a high-throughput, clinically defensible, audit-ready surgical video annotation program—driving automation-first workflows, quality (IRR/QA), and on-time dataset releases. • Stand up and run the annotation “factory” from intake → de-ID → task orchestration → annotation → QA/IRR → adjudication → dataset release + evidence pack. • Partner with clinical SMEs to operationalize a procedure-specific ontology (phases/steps, tools, anatomy, events) and convert it into clear labeling guidelines and UI rules. • Drive pre-labeling + verification workflows, implement routing based on model confidence/uncertainty, and continuously reduce human effort per labeled minute. • Implement and enforce multi-rater sampling strategy IRR reporting by label type (kappa/alpha; IoU/Dice where applicable), calibration loops and retraining for annotators QA gates + sampling plans with acceptance thresholds. • Run the disagreement workflow, manage escalation to senior annotators/clinical reviewers, and ensure guideline updates close recurring issues. • Own versioning, provenance, and release discipline—ensuring every dataset is reproducible and ships with an audit-ready Evidence Pack.

• Define and execute the strategic roadmap for the Data & AI practice, aligning with evolving trends in Lakehouse, cloud-native platforms, and AI engineering. • Build reusable solution accelerators (e.g., Iceberg ingestion templates, Spark ETL frameworks, MLflow pipelines). • Establish and manage strategic partnerships with Databricks, AWS, Azure, and other ecosystem players. • Represent the practice in industry forums, client workshops, and thought leadership initiatives. • Architect scalable Lakehouse solutions using Delta Lake or Iceberg, optimized for performance and cost. • Lead the design of unified batch + streaming pipelines using Spark Structured Streaming and Scala. • Oversee multi-cloud data platform modernization using Azure Data Fabric, EMR, and Redshift. • Guide implementation of data governance frameworks using Unity Catalog, Azure Purview, and Iceberg metadata. • Mentor and grow a high-performing team of data engineers, ML engineers, and platform specialists. • Engage with clients to understand business challenges and translate them into scalable data solutions.

• You own top-of-funnel growth and sourced pipeline, using AI to exponentially extend your strategic and execution capacity. • Design the demand engine, not merely running campaigns. • Operate at the intersection of strategy, systems, and scale. • Set direction, build AI-powered workflows, and ensure consistent pipeline contribution. • Measure success on ICP-qualified meetings, SQLs, and marketing-sourced pipeline.

• Design and develop CLI tools, scripts, and internal utilities to automate repetitive tasks across the data platform, including: • Pipeline execution and orchestration • Data governance workflows • Metadata synchronization • Environment setup and configuration • Test harness development • Automate workflows on Databricks, including: • Job deployment and scheduling • Environment provisioning • MLOps processes using APIs, Terraform, or Databricks SDK • Build and implement robust testing frameworks: • Integration testing for pipelines • End-to-end validation of ETL/ELT workflows • Testing and validation for ML inference workflows • Improve overall productivity, scalability, and reliability of the data and ML engineering ecosystem • Develop lightweight internal tools and dashboards using frameworks such as React, Streamlit, or similar technologies to: • Visualize data pipelines and workflows • Demonstrate model inference capabilities • Provide configuration and operational controls • Enable internal productivity monitoring and dashboards • Collaborate with cross-functional teams to identify automation opportunities and implement best practices

• Design and build AI agent pipelines and maintain RAG systems • Integrate and optimize Large Language Models and structured output workflows • Design, develop, and deploy product features and scalable backends • Collaborate with clients and stakeholders to translate business requirements into software solutions • Own observability and quality metrics for AI systems

• Act as the technical lead for a scrum team building software • Design and evolve end-to-end architecture • Lead integration with internal teams • Provide technical guidance to engineers

• Coordinate QA activities across all Neio Project products • Own QA planning, test execution tracking, and status reporting • Review test cases, automation scripts, and execution results • Act as primary QA contact for product and business teams • Track QA metrics and identify testing risks early

• Understand product requirements, user workflows, acceptance criteria, and business rules across Neio Project applications. • Design, document, and execute test scenarios, test cases, and test data. • Perform functional, regression, integration, smoke, sanity, and end-to-end testing. • Build and maintain UI automation test suites using Selenium or Playwright. • Automate API test cases using Rest Assured with a structured automation framework. • Perform manual API testing and validation using Postman. • Validate REST API requests, responses, payloads, authentication, authorization, status codes, error handling, and API contracts. • Work with development, product, business, and AI teams to clarify requirements and improve test coverage. • Identify, log, track, retest, and close defects using Jira, Azure DevOps, or similar tools. • Validate AI-driven workflows such as chatbot responses, RAG-based responses, document extraction, document synthesis, generated letters, summaries, and workflow outputs. • Prepare test execution reports, defect reports, QA status updates, and release readiness inputs. • Support automation execution through CI/CD pipelines where applicable. • Contribute to QA best practices, reusable automation components, and quality improvement initiatives.

• Coordinate QA activities across all Neio Project products • Own QA planning, work allocation, and test execution tracking • Guide QA resources on test coverage and automation standards • Review test cases and execution results • Ensure timely QA sign-off inputs for releases