Senior Machine Learning Engineer

Job DescriptionS&P Enterprise Data OrganizationSenior ML Engineer Job DescriptionThe 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.What’s 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 deploymentResponsibilities:Design, Develop and Deploy ML powered products and pipelinesMentor a team of Senior and Junior data scientists / ML Engineers in delivering large scale projectsPlay a central role in all stages of the AI product development life cycle, including:Designing Machine Learning systems and model scaling strategiesResearch & Implement ML and Deep learning algorithms for productionRun necessary ML tests and benchmarks for model validationFine-tune, retrain and scale existing model deploymentsExtend existing ML library’s and write packages for reproducing componentsPartner with business leaders, domain experts, and end-users to gain business understanding, data understanding, and collect requirementsInterpret results and present them to business leadersManage production pipelines for enterprise scale projectsPerform code reviews & optimization for your projects and teamLead and mentor by example, including project scrumsTechnical Requirements:Proven track record as a senior / lead ML engineerExpert proficiency in Python (Numpy, Pandas, Spacy, Sklearn, Pytorch/TF2, HuggingFace etc.)Excellent exposure to large scale model deployment strategies and toolsExcellent knowledge of ML & Deep Learning domainSolid exposure to Information Retrieval, Web scraping and Data Extraction at scaleExposure to the following technologies - R-Shiny/Dash/Streamlit, SQL, Airflow, Redis, Celery, Flask/Django/FastAPI, ScrapyExperience with SOTA models related to NLP and expertise in text matching techniques, including sentence transformers, word embeddings, and similarity measuresOpen to learning new technologies and programming languages as requiredA Master’s / PhD from a recognized institute in a relevant specializationGood to have:5+ years of relevant experience in ML EngineeringPrior substantial experience from the Economics/Financial industryPrior work to show on Github, Kaggle, StackOverflow etc.