Senior ML Engineer

Job Description

The Team : As a member of the Data Transformation - Cognitive Engineering team you will work on building and deploying ML powered products and capabilities to power natural language understanding, data extraction, information retrieval and data sourcing solutions for S&P Global Market Intelligence and our clients. You will spearhead deployment of AI products and pipelines while leading-by-example in a highly engaging work environment. You will work in a (truly) global team and encouraged for thoughtful risk-taking and self-initiative. Whats in it for you: Be a part of a global company and build solutions at enterprise scale Lead a highly skilled and technically strong team (including leadership) Contribute to solving high complexity, high impact problems Build production ready pipelines from ideation to deployment Responsibilities: Design, Develop and Deploy ML powered products and pipelines Mentor a team of Senior and Junior data scientists ML Engineers in delivering large scale projects Play a central role in all stages of the AI product development life cycle, including: Designing Machine Learning systems and model scaling strategies Research & Implement ML and Deep learning algorithms for production Run necessary ML tests and benchmarks for model validation Fine-tune, retrain and scale existing model deployments Extend existing ML librarys and write packages for reproducing components Partner with business leaders, domain experts, and end-users to gain business understanding, data understanding, and collect requirements Interpret results and present them to business leaders Manage production pipelines for enterprise scale projects Perform code reviews & optimization for your projects and team Lead and mentor by example, including project scrums Technical Requirements: Proven track record as a senior lead ML engineer Expert proficiency in Python (Numpy, Pandas, Spacy, Sklearn, Pytorch/TF2, HuggingFace etc.) Excellent exposure to large scale model deployment strategies and tools Excellent knowledge of ML & Deep Learning domain Solid exposure to Information Retrieval, Web scraping and Data Extraction at scale Exposure to the following technologies - R-Shiny/Dash/Streamlit, SQL, Docker, Airflow, Redis, Celery, Flask/Django/FastAPI, PySpark, Scrapy Experience with SOTA models related to NLP and expertise in text matching techniques, including sentence transformers, word embeddings, and similarity measures Open to learning new technologies and programming languages as required A Masters PhD from a recognized institute in a relevant specialization Good to have: 6-7+ years of relevant experience in ML Engineering Prior substantial experience from the Economics/Financial industry Prior work to show on Github, Kaggle, StackOverflow etc.