Generative AI Developer / Engineer/Analyst

Accountabilities: Generative AI Development: Design, build, and optimize end-to-end applications using internal and private Large Language Model platforms, ensuring reliable and scalable performance. Prompt Engineering: Create, test, refine, and maintain high-quality prompts to generate accurate, consistent, and contextually relevant outputs. Advanced AI Techniques: Implement advanced prompting methodologies, including few-shot prompting, prompt chaining, and Retrieval-Augmented Generation (RAG). Prompt Evaluation: Establish and maintain prompt testing, evaluation, documentation, and version-control practices to continuously improve output quality. Full-Stack Application Development: Develop complete GenAI applications, including backend services, APIs, and web-based user interfaces, integrating AI capabilities into existing systems and workflows. Enterprise Integration: Build backend services that connect LLMs with enterprise applications, databases, and internal platforms through REST APIs and service-oriented architectures. Web Application Development: Work with modern frontend frameworks and backend technologies to deliver secure, maintainable, and scalable applications. Data Analysis and Processing: Analyze structured and unstructured data, perform data validation and transformation, and prepare information to support AI-powered workflows and pipelines. Business Requirements Translation: Collaborate with business stakeholders to understand operational challenges and translate requirements into practical LLM-driven solutions. Automation Collaboration: Work with automation teams to identify opportunities for integrating Generative AI into existing processes and improving operational efficiency. Security and Governance: Ensure solutions comply with enterprise security, privacy, compliance, and governance standards, particularly when working with confidential business information. Technical Documentation: Document application architecture, prompt strategies, design decisions, and system behavior to support maintainability and knowledge sharing. Requirements Professional Experience: 2–3 years of hands-on experience developing Generative AI or LLM-based applications. Prompt Engineering: 1–3 years of practical experience designing, testing, and optimizing prompts for AI-powered applications. Data or Business Analysis: 2–3 years of experience in data analysis, business analysis, or analytics-focused roles. Programming Skills: Proficiency in at least one modern programming language, such as Python, Java, Node.js, .NET, or an equivalent technology. End-to-End Development: Demonstrated experience building complete applications rather than working exclusively on standalone models, prototypes, or scripts. LLM Platforms: Practical experience using internal, private, or enterprise LLM platforms. RAG and Vector Search: Understanding of Retrieval-Augmented Generation architectures, vector databases, and semantic search concepts. API and Integration Knowledge: Experience with REST APIs, backend services, and service-oriented application architectures. Web Technologies: Familiarity with modern frontend frameworks such as React, Angular, Vue, or equivalent technologies. LLM Fundamentals: Understanding of common LLM limitations, including hallucinations, bias, and model drift, as well as techniques for validating and evaluating model outputs. Performance Optimization: Awareness of the trade-offs between AI application costs, response latency, scalability, and overall performance. Communication Skills: Strong written English skills, particularly for prompt development, technical documentation, and communicating AI behavior. Analytical Thinking: Strong problem-solving abilities and the capacity to work comfortably across programming, data, language, and business requirements. Business Orientation: Ability to understand business objectives and develop practical technology solutions that deliver measurable value. Nice to have: Experience with Blue Prism or other Robotic Process Automation (RPA) platforms. Experience developing and maintaining enterprise web applications. Familiarity with LLMOps or MLOps concepts and practices. Knowledge of document-intensive business environments involving contracts, agreements, reports, and other structured or unstructured content. Previous experience working in regulated industries or enterprise technology environments. Ability to explain Generative AI concepts, limitations, and application behavior to both technical and non-technical stakeholders. Benefits Fully remote working opportunity based in Karnataka, India. Full-time employment with the opportunity to contribute to enterprise-level AI initiatives. Hands-on experience building production-grade Generative AI applications. Opportunity to work with secure, private, and internal LLM platforms. Exposure to advanced AI technologies, prompt engineering, and enterprise integration projects. Opportunity to influence the responsible adoption and scaling of Generative AI across business operations. Collaboration with an end-to-end engineering team across development, data, automation, and business functions. Professional growth through exposure to innovative technologies and real-world enterprise challenges. Salary, healthcare coverage, paid leave, and other employment benefits to be confirmed during the recruitment process.