Machine Learning Engineer

Job Description

The financial services industry is currently undergoing a period of unparalleled change. Since the financial crisis, banks and financial institutions have been subjected to more stringent regulations, increased supervision, and overall global economic instability.These changes have not come without a cost and has made these institutions re-evaluate the way they have been doing business traditionally. Moreover, the proliferation of technological advances including disruptive technologies and digital are compoundingthe pressures of maintaining competitiveness, being adaptable and increasing profit margins.Our client is now investing in a new EMEIA Digital and Emerging Technology focused on delivering key Digital Operations programs, build valuable relationships, and develop strong capabilities through both formal training and working with senior mentors andtalented colleagues.The opportunityBecome a leader in Emerging Tech, demonstrating insights, knowledge, and direction on all aspects of Emerging Tech. Share these insights with clients and help them navigate the journey to Digital Operations. Expand your knowledge and capability of EmergingTech by working within a team of experienced experts.Your key responsibilities

• Work for managing the successful design, execution, and measurement of data initiatives across customer-facing engagements requiring advanced automation scenarios.
• Applying data Science, ML algorithms, using standard statistical tools and techniques for solving client business problems.
• Working on advanced automation client use cases.
• Work in Machine Learning, Statistical Modelling, Quantitative Analysis, Forecasting, Data Visualization, recommendation engine.
• Work in design/implementation/consulting of ML models and deploying scoring pipelines at scale. Integrate this with Automation technologies.
• Understanding of Azure ML Studio, Azure Databricks, IBM Watson Studio etc will be required
• Solve client business problem in areas of Finance, HR and other functions. Use technology (right mix of AI, ML and Automation) to help client automate functions and reduce their cost.

Skills and attributes for success

• 4+ years of experience in machine learning, statistical modelling, quantitative analysis, Forecasting, data visualization, recommendation engine etc.
• 2+ years professional experience in software development in languages like Python, spark. Experience in data processing with Python, SQL
• Understanding of natural language processing concepts and python scientific libraries like NLTK, spacy, Gensim, NMT, Extraction, Classification, Intent, Semantics, NER, BERT, Marian,T5, BART, Transformers, Embedding (Word2Vec, ELMO, Glove), Attention Models,LSTM, RNN, OCR/ICR, OpenCV, Contours Segmentation
• Experience in design/implementation/consulting experience training ML models and deploying scoring pipelines at scale. Experience of using tools like Azure ML Studio, Azure Databricks, IBM Watson Studio will be advantage.
• Understanding of Explainable AI.
• Experience with DL toolkits such as MXNet, TensorFlow, Caffe and Torch.
• Deployment- Pipelines, Flask, Django,Fast Api, TensorFlow Servings, Django, Micro services, Dockers, Kube-Flow
• Experience with source control GIT
• Experience in .Net/node.js/Java or web technologies will be added advantage.
• Understanding of contact center related technologies/speech analytics/Speech Modelling, using various digital techniques will be helpful.

To qualify for the role, you must have

1. Overall, 4-6 years of experience in the software engineering industry and/or in a Technology Consulting domain
2. Experience dedicated to client delivery in the engineering space involving variety of technology capabilities, please see below for reference (Non-exhaustive):
   • Experience in machine learning, statistical modelling, quantitative analysis, Forecasting, data visualization, recommendation engine etc.
   • 2+ years professional experience in software development in languages like Python, R, Spark & SQL. Experience in Tensor flow, keras, transformers, Sci-kit learn, python libraries
   • Experience in design/implementation/consulting experience training ML models and deploying scoring pipelines at scale.
   • Experience in web development technologies like java/node.js/C#
   • Understanding of Data warehousing tool & Power BI.
3. Ability to work in a team environment as well as individual contributor
4. Knowledge of professional software engineering practices & best practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations