- Trivandrum
- 3 - 0 Years
- Not disclosed
- Junior Embedded Software Engineer
- Today
Drive new client acquisition and expand business in IT Services. Conduct client meetings, presentations, and negotiations. Manage end-to-end sales cycle from lead generation to deal closure. Build and maintain strong client relationships (CXOs, hiring managers). Achieve and exceed revenue and sales targets. Develop and manage a robust sales pipeline and forecasts. Lead commercial discussions, pricing, and contract closures. Identify new market opportunities and strategic accounts. Collaborate with recruitment/delivery teams for successful execution. Ensure client satisfaction and repeat business growth.
Job Description Salesforce Platform Knowledge – Understanding of Sales Cloud, Service Cloud, Experience Cloud, and custom objects Testing Types – Functional, Regression, UAT, API testing (REST/SOAP), and Integration testing Automation Tools – Selenium, Playwright Apex Testing – Ability to validate triggers, classes, and batch jobs. SOQL/SOSL – Writing queries to validate data in Salesforce. Data Management – Experience with data loading tools (Data Loader, Workbench). Test Management – Using Jira, Xray, Zephyr for test case management. Agile/DevOps Practices – Familiarity with CI/CD pipelines (e.g., GitHub Actions). Communication & Collaboration – Working with admins, developers, and business users. Write and maintain unit tests to ensure high-quality code. Proactive in approach and suggest testing solution Communicate effectively with client technical partners to clarify requirements and align on implementation. Responsibilities include: Define and execute QA strategy, test plans, and scenarios for Salesforce implementations across Sales, Service, and Marketing Clouds. Develop, review, and maintain manual and automated test cases/scripts covering Salesforce configurations, customizations, and integrations. Implement and enhance test automation frameworks (e.g., Selenium, Provar, Copado) and integrate with CI/CD pipelines. Identify, log, and manage defects using tools like JIRA/Azure DevOps, ensuring high-quality releases and adherence to Salesforce best practices.
Brief Description SUMMARY: The AI / ML Engineer will design, develop, and scale AI-powered data products to enhance Worldwide's services and improve the efficiency of clinical research processes. This role focuses on the end-to-end lifecycle of data products, utilizing the Databricks platform and a variety of advanced LLM tools to create innovative solutions that drive business value. RESPONSIBILITIES: Tasks may include but are not limited to: • Collaborate with cross-functional teams to identify business needs and translate them into production-ready data products. • Develop, test, and deploy AI models and data pipelines within the Databricks environment using Python and SQL. • Manage the full machine learning lifecycle using Databricks MLOps tools to ensure model versioning, tracking, and seamless deployment. • Leverage Databricks AI and ML offerings, including Genie and Genie Code, to provide natural language interfaces for data exploration. • Build and maintain autonomous agents and workflows using Agentbricks and related frameworks. • Implement and fine-tune solutions using a diverse set of LLMs, including Claude, Gemini, and ChatGPT, to solve complex business problems. • Utilize developer productivity tools such as Claude Code to accelerate the delivery of AI solutions. • Optimize and improve the performance of existing prompts and agentic workflows through advanced prompt engineering. • Stay up-to-date with the latest developments in AI, LLM ecosystems, and Databricks features to identify new opportunities for application. • Translate technical concepts and findings into clear language for non-technical stakeholders. • Provide training and support to end-users on AI-related tools and data products. •Create comprehensive documentation for AI models, code, and MLOps processes to facilitate knowledge sharing and troubleshooting. • Design AI solutions with scalability and security in mind, ensuring they can handle increasing clinical data volumes. • Investigate and resolve issues related to AI model performance, data drift, or system functionality. • Ensure compliance with relevant AI-related regulations and data privacy standards within the clinical trials industry. • Perform other duties as assigned. The duties and responsibilities listed above are representative of the nature and level of work assigned and are not necessarily all inclusive.