GenAI Engg

Job Summary

AI Engineer Rank: Senior Associate The selected candidate ¿ Collaborates with senior data scientists to build, fine tune, and evaluate generative AI models, including large language models (LLMs), for various applications. ¿ Implements data pipelines and workflows to gather, clean, and prepare data from diverse sources for analysis and model training. ¿ Works on developing and deploying machine learning algorithms, ensuring models are optimized for performance and scalability ¿ Assists in translating complex technical findings into actionable insights and recommendations for non technical stakeholders, contributing to impactful business decisions Your key responsibilities include ¿ Implementing AI solutions that integrate seamlessly with existing business systems to enhance functionality and user interaction. ¿ Reviewing complex data sets to establish data quality and highlighting where data cleansing is required to remediate the data ¿ Designing and implementing data models to manage data within the organization, ¿ Migrating data from one system to another using multiple sources, identifying and implementing storage requirements ¿ Analyzing the latest trends such as cloud computing and distributed processing, and their uses in business, building industry knowledge ¿ Peer reviewing models and working with relevant business areas to seek their input and ensuring they are fit for the designed purpose ¿ Automating important infrastructure for the data science team. ¿ Staying current with AI trends and suggesting improvements to existing systems and workflows. Skills and attributes for success ¿ 5+ years¿ experience in machine learning, large scale data acquisition, transformation, and cleaning, both structured and unstructured data ¿ Experience with generative LLM fine tuning and prompt engineering. ¿ Good knowledge with cloud ecosystems, Azure is preferred ¿ Strong programming skills in Python and developing APIs ¿ Knowledge of containerization platforms like Docker is a must. ¿ Experience with Git and modern software development workflow. ¿ Experience with tools in the distributed computing, GPU, cloud platforms, and Big Data domains (e.g., GCP, AWS, MS Azure, Hadoop, Spark, Databricks) is a plus ¿ Experience in ML/NLP algorithms, including supervised and unsupervised learning. ¿ Experience with SQL, Document DBs and Vector DBs ¿ Batch Processing Capability to design an efficient way of processing high volumes of data where a group of transactions is collected over a period Data ¿ Visualization and storytelling Capability to assimilate and present data, as well as more advanced visualization techniques.