Lead Artificial Intelligence/Machine Learning @ IBM India

Job Description

Requirement Name: Lead AI/ML Engineer Location: Bellandur, Bangalore Years of Experience: 6-10 years Company Profile XpressBees a logistics company started in 2015 is amongst the fastest growing companies of its sector. While we started off rather humbly in the space of ecommerce B2C logistics, the last 5 years have seen us steadily progress towards expanding our presence. Our vision to evolve into a strong full-service logistics organization reflects itself in our new lines of business like 3PL, B2B Xpress and cross border operations. Our strong domain expertise and constant focus on meaningful innovation have helped us rapidly evolve as the most trusted logistics partner of India. We have progressively carved our way towards best-in-class technology platforms, an extensive 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. Our big focus areas for. the very near future include strengthening our presence as service providers of choice and leveraging the power of technology to improve efficiencies for our clients. Job Overview XpressBees would enrich and scale its end-to-end logistics solutions at a high pace. This is a great opportunity to join. The team working on forming and delivering the operational strategy behind Artificial Intelligence / Machine Learning and Data Engineering, leading projects and teams of AI Engineers collaborating with Data Scientists. In your role, you will build high performance AI/ML solutions using ground-breaking AI/ML and Big Data technologies. You will need to understand business requirements and convert them to a solvable data science problem statement. You will be involved in end-to-end AI/ML projects, starting from smaller scale POCs all the way to full scale ML pipelines in production. Seasoned AI/ML Engineers would own the implementation and productization of cutting-edge AI driven algorithmic components for search, recommendation and insights to improve the efficiencies of the logistics supply chain and serve the customer better. You will apply innovative ML tools and concepts to deliver value to our teams and customers and make an impact to the 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 and tech support solutions, e.g. chat bots. - Breach detection/prediction An 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 technologies The AI Engineering team enables internal teams to add AI capabilities to their Apps and Workflows easily via APIs without needing to build AI expertise in each team Decision Support, NLP, Computer Vision, for Public Clouds and Enterprise in NLU, Vision and Conversational AI. The candidate is adept at working with large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action. 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 be comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion for 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 and deployment 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 that works 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, effectively communicating your needs and understanding theirs and addressing external and internal shareholder product 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 to drive business solutions. Mine and analyze data from company databases to drive optimization and improvement of product development, 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 metrics and other business 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 and deployment 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, effectively communicating your needs and understanding theirs and address external and internal shareholder's product challenges. Deliver machine learning and data science projects with data science techniques and associated libraries such as AI/ ML or equivalent NLP (Natural Language Processing) packages. Such techniques include a good to phenomenal understanding of statistical models, probabilistic algorithms, classification, clustering, deep learning 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 for successful 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 broadly distributed, 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 insights from large data sets. Experience working with and creating data architectures. Good understanding of various machine learning and natural language processing technologies, such as classification, 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 neural networks, 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 or mathematical 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 down serving 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

Employement Category:

Employement Type: Full time
Industry: Others
Role Category: General / Other Software
Functional Area: Not Applicable
Role/Responsibilies: Lead Artificial Intelligence/Machine Learning

+ View Contact

Keyskills:   Artificial Intelligence Machine Learning Data Engineering Deep Learning NLP Reinforcement Learning Logistic Regression Decision Trees LPs Operations Research Supply Chain Optimization Data Analytics Computer Vision OCR Python Big Data Hadoop HBase Spark R SQL Spark Google Analytics AdWords MapReduce Hive MySQL Kafka Business Objects Tableau Clustering Neural Networks Regression Simulation Scenario Analysis Modeling Decision Trees Relational Databases Airflow Oozie Data Engineering ETL Data Streaming Docker Exploratory Data Analysis SQL Random Forests XGBoost MILPs Stochastic Programs MDPs Pytorch Tensorflow Redshift S3 Digital Ocean Site Catalyst Core metrics Crimson Hexagon Facebook Insights Quicksight Periscope D3 ggplot Data Pipelines NoSQL Databases Machine Learning Lifecycle Statistical Models MLFlow Redshift BigQuery