We are seeking a dedicated Associate to join our Data & Analytics Advisory team, focusing on the development and deployment of cutting-edge Generative AI solutions. In this role, you will bridge the gap between complex data infrastructure and actionable business intelligence, working on high-impact projects that leverage LLMs to solve real-world client challenges. You will be instrumental in building scalable AI applications that drive innovation and operational efficiency across diverse industry sectors.
Key Deliverables
- Develop and deploy AI-based applications leveraging LLMs and Generative AI models such as GPT and Gemini.
- Build and maintain scalable backend systems using Python to ensure seamless integration with frontend components.
- Design, optimize, and fine-tune generative AI models to align with specific client use cases and business requirements.
- Implement RAG (Retrieval-Augmented Generation) techniques and agentic workflows to enhance model accuracy and utility.
- Collaborate with cross-functional teams, including data scientists and product managers, to deliver robust, end-to-end AI solutions.
- Deploy and manage AI applications on cloud platforms like AWS or Azure, ensuring high performance and system scalability.
Essential Requirements
- 4 – 7 years of professional experience in data, analytics, or AI engineering.
- Proficiency in Python for application development and experience with web frameworks such as Flask, FastAPI, or Django.
- Hands-on experience with Generative AI frameworks and libraries, including Hugging Face, OpenAI API, and LangChain.
- Strong knowledge of LLMs, fine-tuning techniques, and MLOps frameworks for model lifecycle management.
- Experience with frontend development using JavaScript and React.
- Familiarity with cloud platforms (AWS or Azure) for deployment and scaling.
- Bachelor's degree in Engineering or MCA; MBA is also accepted.
Preferred Qualifications
- Hands-on experience with DevOps practices, specifically using Jenkins for CI/CD pipelines.
- Knowledge of containerization technologies, including Docker and Kubernetes.
- Experience with PySpark and broader data engineering tools within the Azure or AWS ecosystems.
