Generative AI Full Stack Developer

Generative AI Full Stack Developer-Technical Specialist-App Development

About the job

Birlasoft, a part of the CKA Birla Group, is a leading provider of domain expertise, enterprise solutions, and digital technologies to redefine business processes. With a consultative and design thinking approach, we enable our customers to run businesses with unmatched efficiency and innovation. Our core values focus on Diversity, Equity, and Inclusion (DEI) initiatives, along with Corporate Social Responsibility (CSR) activities, to build inclusive and sustainable communities.

Generative AI Full Stack Developer
Role Summary:
A Generative AI Full Stack Developer responsible for designing, coding, and implementing end-to-end GenAI applications by integrating LLMs (Large Language Models) and SLMs (Small Language Models) into scalable backend and user-friendly frontend solutions. This role emphasizes expertise in open-source frameworks, cloud services, and modern web technologies, with the ability to build robust pipelines, APIs, and interfaces. The developer will deliver production-ready GenAI solutions by combining strong backend engineering, frontend development, and advanced LLM model integration.
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Key Responsibilities
Full Stack Development: Build and maintain backend services (Flask/FastAPI) and frontend interfaces (React) for GenAI applications.
GenAI Model Integration: Design and implement RAG pipelines, Build Agentic AI solutions using LLMs like Azure OpenAI (GPT4o, GPT5 etc), Prompt Engineering.
Frameworks & Libraries: Develop solutions using LangChain, LlamaIndex, AutoGen, or CrewAI to orchestrate multi-step tasks and decision-making workflows and other GenAI frameworks.
Document Intelligence: Implement document intelligence workflows using GenAI base tools, techniques, cloud-native and open-source tools.
Regulatory Compliance: Ensure solutions comply with ICH, EMA, FDA, and 21 CFR Part 11 guidelines; maintain audit trails, traceability, and data integrity for regulatory submissions.
Cloud Deployment: Deploy applications and models on Azure with scalability and monitoring.
Prototyping & Demos: Rapidly prototype GenAI features and demonstrate capabilities to stakeholders.
Data Engineering: Build preprocessing pipelines for training, fine-tuning, and inference workloads.
API Development & Integration: Design and integrate REST APIs for data ingestion and application workflows.
Model Evaluation: Evaluate model performance using defined benchmarks and refine for quality and accuracy.
UI/UX Integration: Collaborate with designers to embed GenAI features into intuitive interfaces.
Version Control & CI/CD: Manage code repositories with Git, and automate deployment pipelines.
Quality Assurance: Debug, optimize, and performance-tune backend and frontend systems.
Documentation: Maintain technical documentation for models, APIs, and application flows.
Cross-Team Collaboration: Work with product managers, data scientists, and QA to deliver end-to-end solutions.
Continuous Learning: Stay current with GenAI advancements, frameworks, and cloud innovations.
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Required Skills
Programming: Proficiency in Python (OOP, FastAPI, Flask) and JavaScript/TypeScript for full-stack development.
Frontend: Hands-on with , , or similar tools for building GenAI-enabled UIs.
Backend & APIs: Strong experience in designing REST APIs, microservices, and backend systems.
GenAI: Skilled in RAG, Agentic AI for customizing LLMs and SLMs.
Frameworks: Expertise with Hugging Face, LangChain, LlamaIndex, CrewAI, or AutoGen.
Cloud Platforms: Experience with Azure (OpenAI, Document Intelligence, Key Vault, Azure AI Foundry, Responsible AI) other AI services.
Data Processing: Ability to create preprocessing pipelines for structured/unstructured data.
Model Evaluation: Familiarity with accuracy, latency, and F1 benchmarks for GenAI applications.
Version Control: Advanced Git knowledge with CI/CD pipelines (GitHub Actions, Azure DevOps, Jenkins).
Security & Compliance: Understanding of data governance, audit trails, and regulatory compliance in AI apps.
Problem Solving: Strong debugging and optimization skills across frontend, backend, and AI workflows.
Collaboration: Ability to work in agile, cross-functional teams.
Rapid Prototyping: Capable of quickly building PoCs and converting them into production solutions.
Learning Mindset: Passion for keeping up with evolving GenAI, cloud, and web development trends.