Job
Description
Key Areas of Responsibility
Participate in design discussions about the technical implementation and consider the tradeoffs to support business value, scalability and delivery timeline.
Implement, and optimize machine learning algorithms and models to solve specific business problems or improve existing processes. Collect, clean, and preprocess large datasets for training and evaluation. Perform exploratory data analysis to gain insights and inform feature engineering. Train machine learning models using various techniques such as supervised, unsupervised, and reinforcement learning. Evaluate model performance using appropriate metrics and iterate on model design as needed. Integrate models and GenAI services with existing software infrastructure and workflows. Optimize the performance of machine learning models and algorithms, considering factors such as speed, accuracy, memory usage, and scalability. Collaborate with cross-functional teams including Model Discovery Team, DxP, IDP, Horizontal/Vertical Solutions Team(s), Infrastructure Teams etc., Stay updated with the latest advancements in machine learning research and technologies. Experiment with new algorithms, frameworks, and tools to improve model performance and enhance capabilities. Document code, algorithms, and methodologies to facilitate knowledge sharing and maintain codebase integrity. Contribute to peer/code review, internal knowledge repositories and participate in knowledge-sharing sessions to disseminate best practices and lessons learned.
Skill required:
Candidate with 5+ years of relevant experience in Machine Learning, AI, Python as mandatory skills Strong communication, collaboration and problem solving skills with a track record of delivering production grade systems in a team environment Motivated individual who learns quickly, has pride in building a new product and can engage others to accelerate technical solutions 2+ years of experience in AI & ML, Python and working with agile scrum methodologies. Strong DL for cases like image/audio/text classification. Strong NLP (LLM) knowledge on entity extraction. Multi-lingual LM and multi-modal LM experience are extremely preferred. Hands on experience on GenAI (LLM), and experts on prompt engineering. Machine learning pipeline knowledge and hands-on experience on Kubeflow or MLflow frameworks. This is highly desirable but not required. Experience in MLOps, Cloud, Python, Kubernetes, Workflows, MongoDB, PostGresDB Experience with relational and non-relational databases (Postgres, MongoDB, GraphDB,
VectorDB) 1+ year of experience with Google Cloud Platform, AWS or Azure Experience with agile tools such as Atlassian JIRA, Rally, TFS or Version One Experience of CI/CD tools (like Gitlab, Jenkins, git), Docker, Linux Shell scripting.
