AI/ML Engineer (LLM)

Job Description

Role & responsibilities The AI/ML Engineer will be responsible for the development and implementation of open-source large language models (LLM) tailored for the Banking, Financial Services, and Insurance (BFSI) sector. You will work collaboratively with cross-functional teams to design innovative solutions that enhance our product offerings, enabling more efficient customer interactions and data insights. Your analytical skills will help in improving the performance and scalability of LLM applications while ensuring adherence to compliance and security standards. Responsibilities Develop and implement open-source LLMs for BFSI applications. Collaborate with product managers, data scientists, and software engineers to define project objectives and deliver high-quality solutions. Conduct experiments and optimize model performance using real-world datasets. Monitor and evaluate model effectiveness in production and refine models based on feedback. Stay up-to-date with the latest advancements in AI/ML to incorporate relevant best practices into the development process. Contribute to documentation and knowledge base for models and methodologies. Requirements Bachelors degree or equivalent experience in Computer Science, Engineering, or a related field. Minimum of 3+ years of experience in AI/ML development, preferably in the BFSI industry, [With hands on coding and interview will be technical, you need to demonstrate your expertise in coding without AI agents and search engine You must have built and deployed AI/ML solutions to production Proficient in programming languages such as Python and also preferably typescript and java ; Python is core requirement . Proficiency in flask,fast api framework and have built application which is compliant to security standards and performant and scalable Able to work with rdbms and nosql databases and vector databases Experience with open-source ML frameworks (e.g., TensorFlow, PyTorch). Industry experience with open-source LLM frameworks (e.g., Langchain, ollama etc...). Solid understanding of machine learning algorithms, data structures, and statistical methods. Familiarity with data preprocessing, feature engineering, and model evaluation techniques. Excellent problem-solving skills and ability to work in a fast-paced environment. Strong communication skills to convey complex technical concepts to non-technical stakeholders