Responsibilities:

• Develop and maintain web applications utilizing modern frontend and backend technologies.
• Collaborate with the AI research team to integrate generative AI models into web applications.
• Design and implement responsive user interfaces that provide an exceptional user experience
• Build and optimize RESTful APIs and GraphQL endpoints for seamless communication between the frontend and backend.
• Implement robust authentication and authorization mechanisms to ensure data security and user privacy.
• Deploy and manage web applications on cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Work closely with product managers and designers to translate product requirements into technical specifications.
• Participate in code reviews, troubleshooting, and debugging to maintain code quality and reliability.
• Stay updated on the latest trends and advancements in web development, AI, and machine learning.

Requirements

• Bachelor’s degree in computer science, engineering, or related field.
• 1-4 years of experience as a Backend/Full Stack Engineer, with a strong focus on web development.
• Solid understanding of backend development using Python.
• Experience working with databases such as MySQL, MongoDB, or PostgreSQL.
• Familiarity with cloud platforms and services (AWS, Azure, GCP) for deployment and scaling.
• Strong problem-solving skills and attention to detail.
• Excellent communication and collaboration abilities.
• Ability to thrive in a fast-paced startup environment and adapt to changing priorities.
• [Bonus] Experience with generative AI technologies

In-depth Business Problem Analysis

• Understand Business Problems: Work closely with stakeholders across different industries to gather and deeply understand business problems, objectives, and requirements.
• Clarifying Questions: Ask thoughtful, clarifying questions to ensure all aspects of the business problem are well-understood and effectively communicated to the technical teams.
• Process Understanding & Data Modeling: Leverage your data modeling experience to understand the underlying business processes and how they relate to AI-driven solutions, helping to structure data for optimal platform performance.

 Output Evaluation (Manual & Automated)

• Manual Evaluation of AI Outputs: Conduct thorough manual reviews of AI-generated outputs to verify that they meet business requirements and deliver the expected value.
• Automated Evaluation Processes: Collaborate with engineering teams to design and implement automated evaluation methods to streamline the validation of AI outputs, using metrics and KPIs.
• Continuous Improvement: Provide ongoing feedback to enhance AI model outputs and ensure our platform continuously evolves to meet the needs of multiple business verticals.

Stakeholder Collaboration

• Multi-Vertical Expertise: Work with clients and internal teams across various industry verticals to ensure that AI solutions are customized and adaptable to diverse business contexts.
• Liaison Between Teams: Act as a bridge between business and technical teams, ensuring that the platform’s capabilities align with stakeholder needs across different sectors.
• Drive Product Refinement: Based on your evaluations and business insights, provide feedback and recommendations for product enhancements, ensuring solutions are effective across multiple verticals.

Process Optimization and Continuous Improvement

• Optimize AI Integration: Collaborate with product, engineering, and data science teams to ensure smooth integration of AI solutions into existing business workflows, identifying opportunities for improvement.
• Data-Driven Workflow Optimization: Analyze existing workflows and propose AI-driven automation opportunities to enhance efficiency and outcomes for clients in various industries.
• Best Practices: Develop best practices for evaluating and implementing Generative AI solutions, ensuring consistent results across different business problems and verticals.

Data and Metrics Orientation

• Data Modeling & Analysis: Use data modeling and analysis techniques to understand how processes are related and inform the structure of AI solutions.
• Performance Tracking: Establish and track performance metrics for AI models to ensure continuous improvement and alignment with business goals.

Required Skills & Experience:

• Experience: 5-8 years of hands-on experience in business analysis with a strong emphasis on data modeling and solving complex business challenges. Ideal candidates will have a technical background before transitioning into a business analysis or product owner role, with mid-level experience in AI or technology-driven environments considered a plus.
• Data Modeling Expertise: In-depth knowledge of data modeling techniques, with an understanding of how business processes align with data structures and how AI can leverage this relationship. Skilled at identifying the data requirements for each step of the business process and proficient in effectively modeling these processes.
• Multi-Vertical Exposure: Preferred experience working across multiple industry verticals, demonstrating an ability to adapt AI solutions to a variety of business contexts.
• Analytical & Problem-Solving Skills: Demonstrated expertise in deconstructing complex business challenges into actionable tasks, delivering data-driven recommendations. Skilled in understanding customer requirements and translating them into clear, well-defined user stories.
• Communication & Collaboration: Excellent communication skills, with the ability to articulate business requirements to technical teams and explain AI solutions to business stakeholders.
• Exposure to AI Platforms: Familiarity with AI platforms or machine learning technologies is a plus, but not required. The ideal candidate will be eager to learn AI-specific technologies and contribute to AI-driven initiatives.

Preferred Qualifications:

• Experience with enterprise SaaS or Software products or AI-driven platforms.
• Experience working in a professional services / consulting company
• Exceptional documentation and process improvement skills in diverse, multi-vertical environments, with a keen ability to identify automation opportunities and develop tools/utilities to streamline implementations.
• Hands-on experience with tools and techniques for automated evaluation of AI outputs.
• Familiarity with data analysis tools such as SQL, Excel, or BI tools is a plus.