About the Company: Birlasoft, a global leader at the forefront of Cloud, AI, and Digital technologies, seamlessly blends domain expertise with enterprise solutions. The company's consultative and design-thinking approach empowers societies worldwide, enhancing the efficiency and productivity of businesses. As part of the multibillion-dollar diversified CKA Birla Group, Birlasoft with its 12,000+ professionals, is committed to continuing the Group's 170-year heritage of building sustainable communities.
About the Role: Developer-GenAI Application shall be focused on coding, developing, and implementing GenAI applications using fine-tuned LLMs (Large Language Models) and SLMs (Small Language Models). This role emphasizes hands-on expertise in open-source frameworks, libraries, and cloud tools, with the ability to quickly prototype and demonstrate GenAI applications. The developer shall work under the guidance of senior team members to deliver high-quality, scalable, and innovative solutions.
Responsibilities:
- Coding and Development: Write clean, using Lang chain/Lang graph and Auto Gen frameworks efficiently, and maintainable code for GenAI applications using Python and open-source frameworks.
- Fine-Tuning Models: Fine-tune LLMs and SLMs using techniques like PEFT, LoRA, and QLoRA for specific use cases.
- Open-Source Frameworks: Work with frameworks like Hugging Face, LangChain, and others to build GenAI solutions.
- Azure AI Expertise: Design and deploy scalable AI solutions leveraging a comprehensive suite of Azure AI services.
- Integration and Deployment: Integrate generative AI models into existing enterprise systems and applications. Implement robust MLOps practices, CI/CD pipelines (e.g., Azure DevOps, GitHub, Jenkins), and containerization (Docker, Kubernetes) for seamless deployment. Knowledge of AWS would be an added advantage.
- Data Preprocessing: Build and maintain data preprocessing pipelines for training and fine-tuning models.
- API Integration: Integrate REST, SOAP, and other APIs for data ingestion, processing, and output delivery.
- Model Evaluation: Evaluate model performance using metrics and benchmarks and iterate to improve results.
- Prototyping: Quickly prototype and demonstrate GenAI applications to showcase capabilities and gather feedback.
- Front-End Development: Collaborate with front-end developers to integrate GenAI capabilities.
