Lead Artificial Intelligence Engineer

Company ProfileXressBees – a logistics company started in 2015 – is amongst the fastest growing companies of its sector. Ourvision to evolve into a strong full-service logistics organization reflects itself in the various lines of business like B2Clogistics 3PL, B2B Xpress, Hyperlocal and Cross border Logistics.Our strong domain expertise and constant focus on innovation has helped us rapidly evolve as the most trustedlogistics partner of India. XB has progressively carved our way towards best-in-class technology platforms, anextensive logistics network reach, and a seamless last mile management system.While on this aggressive growth path, we seek to become the one-stop-shop for end-to-end logistics solutions. Ourbig focus areas for the very near future include strengthening our presence as service providers of choice andleveraging the power of technology to drive supply chain efficiencies.Job OverviewXpressBees would enrich and scale its end-to-end logistics solutions at a high pace. This is a great opportunity to jointhe team working on forming and delivering the operational strategy behind Artificial Intelligence / Machine Learningand Data Engineering, leading projects and teams of AI Engineers collaborating with Data Scientists. In your role, youwill build high performance AI/ML solutions using groundbreaking AI/ML and BigData technologies. You will need tounderstand business requirements and convert them to a solvable data science problem statement. You will beinvolved in end to end AI/ML projects, starting from smaller scale POCs all the way to full scale ML pipelines inproduction.Seasoned AI/ML Engineers would own the implementation and productionzation of cutting-edge AI driven algorithmiccomponents for search, recommendation and insights to improve the efficiencies of the logistics supply chain andserve the customer better.You will apply innovative ML tools and concepts to deliver value to our teams and customers and make an impact tothe organization while solving challenging problems in the areas of AI, ML , Data Analytics and Computer Science.Opportunities for application:- Route Optimization- Address / Geo-Coding Engine- Anomaly detection, Computer Vision (e.g. loading / unloading)- Fraud Detection (fake delivery attempts)- Promise Recommendation Engine etc.- Customer & Tech support solutions, e.g. chat bots.- Breach detection / predictionAn Artificial Intelligence Engineer would apply himself/herself in the areas of -- Deep Learning, NLP, Reinforcement Learning- Machine Learning - Logistic Regression, Decision Trees, Random Forests, XGBoost, etc..- Driving Optimization via LPs, MILPs, Stochastic Programs, and MDPs- Operations Research, Supply Chain Optimization, and Data Analytics/Visualization- Computer Vision and OCR technologiesThe AI Engineering team enables internal teams to add AI capabilities to their Apps and Workflows easily via APIswithout needing to build AI expertise in each team – Decision Support, NLP, Computer Vision, for Public Clouds andEnterprise in NLU, Vision and Conversational AI.Candidate is adept at working with large data sets to findopportunities for product and process optimization and using models to test the effectiveness of different courses ofaction. They must have knowledge using a variety of data mining/data analysis methods, using a variety of data tools,building, and implementing models, using/creating algorithms, and creating/running simulations. They must becomfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passionfor discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.Roles & Responsibilities● Develop scalable infrastructure, including microservices and backend, that automates training anddeployment of ML models.● Building cloud services in Decision Support (Anomaly Detection, Time series forecasting, Fraud detection,Risk prevention, Predictive analytics), computer vision, natural language processing (NLP) and speech thatwork out of the box.● Brainstorm and Design various POCs using ML/DL/NLP solutions for new or existing enterprise problems.● Work with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectivelycommunicating your needs and understanding theirs and address external and internal shareholder'sproduct challenges.● Build core of Artificial Intelligence and AI Services such as Decision Support, Vision, Speech, Text, NLP, NLU,and others.● Leverage Cloud technology –AWS, GCP, Azure● Experiment with ML models in Python using machine learning libraries (Pytorch, Tensorflow), Big Data,Hadoop, HBase, Spark, etc● Work with stakeholders throughout the organization to identify opportunities for leveraging company data todrive business solutions.● Mine and analyze data from company databases to drive optimization and improvement of productdevelopment, marketing techniques and business strategies.● Assess the effectiveness and accuracy of new data sources and data gathering techniques.● Develop custom data models and algorithms to apply to data sets.● Use predictive modeling to increase and optimize customer experiences, supply chain metric and otherbusiness outcomes.● Develop company A/B testing framework and test model quality.● Coordinate with different functional teams to implement models and monitor outcomes.● Develop processes and tools to monitor and analyze model performance and data accuracy.● Develop scalable infrastructure, including microservices and backend, that automates training anddeployment of ML models.● Brainstorm and Design various POCs using ML/DL/NLP solutions for new or existing enterprise problems.● Work with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectivelycommunicating your needs and understanding theirs and address external and internal shareholder'sproduct challenges.● Deliver machine learning and data science projects with data science techniques and associated librariessuch as AI/ ML or equivalent NLP (Natural Language Processing) packages. Such techniques include a goodto phenomenal understanding of statistical models, probabilistic algorithms, classification, clustering, deeplearning or related approaches as it applies to financial applications.● The role will encourage you to learn a wide array of capabilities, toolsets and architectural patterns forsuccessful delivery.What is required of you?You will get an opportunity to build and operate a suite of massive scale, integrated data/ML platforms in a broadlydistributed, multi-tenant cloud environment.● B.S., M.S., or Ph.D. in Computer Science, Computer Engineering● Coding knowledge and experience with several languages: C, C++, Java,JavaScript, etc.● Experience with building high-performance, resilient, scalable, and well-engineered systems● Experience in CI/CD and development best practices, instrumentation, logging systems● Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insightsfrom large data sets.● Experience working with and creating data architectures.● Good understanding of various machine learning and natural language processing technologies, such asclassification, information retrieval, clustering, knowledge graph, semi-supervised learning and ranking.● Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest,Boosting, Trees, text mining, social network analysis, etc.● Knowledge on using web services: Redshift, S3, Spark, Digital Ocean, etc.● Knowledge on creating and using advanced machine learning algorithms and statistics: regression,simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.● Knowledge on analyzing data from 3rd party providers: Google Analytics, Site Catalyst, Core metrics,AdWords, Crimson Hexagon, Facebook Insights, etc.● Knowledge on distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, MySQL, Kafka etc.● Knowledge on visualizing/presenting data for stakeholders using: Quicksight, Periscope, Business Objects,D3, ggplot, Tableau etc.● Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neuralnetworks, etc.) and their real-world advantages/drawbacks.● Knowledge of advanced statistical techniques and concepts (regression, properties of distributions,statistical tests, and proper usage, etc.) and experience with applications.● Experience building data pipelines that prep data for Machine learning and complete feedback loops.● Knowledge of Machine Learning lifecycle and experience working with data scientists● Experience with Relational databases and NoSQL databases● Experience with workflow scheduling / orchestration such as Airflow or Oozie● Working knowledge of current techniques and approaches in machine learning and statistical ormathematical models● Strong Data Engineering & ETL skills to build scalable data pipelines. Exposure to data streaming stack (e.g.Kafka)● Relevant experience in fine tuning and optimizing ML (especially Deep Learning) models to bring downserving latency.● Exposure to ML model productionzation stack (e.g. MLFlow, Docker)● Excellent exploratory data analysis skills to slice & dice data at scale using SQL in Redshift/BigQuery.