ML & NLP Engineer

Responsibilities and Tasks:

• Understand the Business Problem and the Relevant Data

• Maintain an intimate understanding of company and department strategy

• Translate analysis requirements into data requirements

• Identify and understand the data sources that are relevant to the business problem

• Develop conceptual models that capture the relationships within the data

• Define the data-quality objectives for the solution

• Be a subject matter expert in data sources and reporting options

Architect Data Management Systems:

• Use understanding of the business problem and the nature of the data to select appropriate data management system (Big Data, Cloud DW,Cloud (GCP/AWS/Azure), OLTP, OLAP, etc.)

• Design and implement optimum data structures in the appropriate data management system (Cloud DW, Cloud (GCP/AWS/Azure), Hadoop, SQL Server/Oracle, etc.) to satisfy the data requirements

• Plan methods for archiving/deletion of information

Develop, Automate, and Orchestrate an Ecosystem of ETL Processes for Varying Volumes of Data:

• Identify and select the optimum methods of access for each data source (real-time/streaming, delayed, static)

• Determine transformation requirements and develop processes to bring structured and unstructured data from the source to a new physical data model

• Develop processes to efficiently load the transform data into the data management system

Prepare Data to Meet Analysis Requirements

• Work with the data scientist to implement strategies for cleaning and preparing data for analysis (e.g., outliers, missing data, etc.)

• Develop and code data extracts

• Follow standard methodologies to ensure data quality and data integrity

• Ensure that the data is fit to use for data science applications

• Qualifications and Experience:

• 5 - 9 years of experience developing, delivering, and/or supporting data engineering, advanced analytics or business intelligence solutions

• Experienced in developing ETL/ELT processes using Apache Ni-Fi and Snowflake

• Significant experience with big data processing and/or developing applications and data sources via Hadoop, Yarn, Hive, Pig, Sqoop, MapReduce, HBASE, Flume, etc.

• Significant experience with big data processing and/or developing applications and data Pipelines via Hadoop, Yarn, Hive, Spark, Pig, Sqoop, MapReduce, HBASE, Flume, etc.

• Data Engineering and Analytics on Google Cloud Platform using BigQuery, Cloud Storage, Cloud SQL, Cloud Pub/Sub, Cloud DataFlow, Cloud Composer..etc or equivalent cloud platform.

• Familiarity with software architecture (data structures, data schemas, etc.)

• Strong working knowledge of databases (Oracle, MSSQL, etc.) including SQL and NoSQL.

• Strong mathematics background, analytical, problem solving, and organizational skills

• Strong communication skills (written, verbal and presentation)

• Experience working in a global, multi-functional environment

• Minimum of 2 years’ experience in any of the following: At least one high-level client, object-oriented language (e.g. JAVA/Python/Perl/Scala, etc.); at least one or more web programming language (PHP, MySQL, Python, Perl, JavaScript etc.); one or more Data Extraction Tools (Apache NiFi/Informatica/Talend etc.)

• Software development using programming languages like Python/Java/Scala

• Ability to travel as needed

• Role: Machine Learning Lead
• Experience: 5+ Years
• Employee strength: 80+
• Remuneration: Most competitive in the market

Programming Language:

• Advance knowledge of Python.

• Object Oriented Programming skills.

Conceptual:

• Mathematical understanding of machine learning and deep learning algorithms.

• Thorough grasp on statistical terminologies.

Applied:

• Libraries: Tensorflow, Keras, Pytorch, Statsmodels, Scikit-learn, SciPy, Numpy, Pandas, Matplotlib, Seaborn, Plotly

• Algorithms: Ensemble Algorithms, Artificial Neural Networks and Deep Learning, Clustering Algorithms, Decision Tree Algorithms, Dimensionality Reduction Algorithms, etc.

• MySQL, MongoDB, ElasticSearch or other NoSQL database implementations.

If interested kindly share your cv at tanya @tigihr. com

- Modeling complex problems, discovering insights, and identifying opportunities through the use of statistical, algorithmic, mining, and visualization techniques

- Experience working with business understanding the requirement, creating the problem statement, and building scalable and dependable Analytical solutions

- Must have hands-on and strong experience in Python

- Broad knowledge of fundamentals and state-of-the-art in NLP and machine learning

- Strong analytical & algorithm development skills

- Deep knowledge of techniques such as Linear Regression, gradient descent, Logistic Regression, Forecasting, Cluster analysis, Decision trees, Linear Optimization, Text Mining, etc

- Ability to collaborate across teams and strong interpersonal skills

Skills

- Sound theoretical knowledge in ML algorithm and their application

- Hands-on experience in statistical modeling tools such as R, Python, and SQL

- Hands-on experience in Machine learning/data science

- Strong knowledge of statistics

- Experience in advanced analytics / Statistical techniques – Regression, Decision trees, Ensemble machine learning algorithms, etc

- Experience in Natural Language Processing & Deep Learning techniques 

- Pandas, NLTK, Scikit-learn, SpaCy, Tensorflow

About Us:

We are a VC-funded startup solving one of the biggest transportation problems India faces. Most passengers in India travel long distance by IRCTC trains. At time of booking, approx 1 out of every 2 passengers end up with a Waitlisted or RAC ticket. This creates a lot of anxiety for passengers, as Railway only announces only 4 hour before departure if they have a confirmed seat. We solve this problem through our Waitlist & RAC Protection. Protection can be bought against each IRCTC ticket at time of booking. If train ticket is not confirmed, we fly the passenger to the destination. Our team consists of 3 Founders from IIT, IIM and ISB.

Functional Experience:

• Computer Science or IT Engineering background with solid understanding of basics of Data Structures and Algorithms
• 2+ years of data science experience working with large datasets
• Expertise in Python packages like pandas, numPy, sklearn, matplotlib, seaborn, keras and tensorflow
• Expertise in Big Data technologies like Hadoop, Cassandra and PostgreSQL
• Expertise in Cloud computing on AWS with EC2, AutoML, Lambda and RDS
• Good knowledge of Machine Learning and Statistical time series analysis (optional)
• Unparalleled logical ability making you the go to guy for all things related to data
• You love coding like a hobby and are up for a challenge!

Cultural:

• Assume a strong sense of ownership of analytics : Design, develop & deploy
• Collaborate with senior management, operations & business team
• Ensure Quality & sustainability of the architecture
• Motivation to join an early stage startup should go beyond compensation

Job Title: Chief Engineer: Deep Learning Compiler Expert

You will collaborate with experts in machine learning, algorithms and software to lead our effort of deploying machine learning models onto Samsung Mobile AI platform.

In this position, you will contribute, develop and enhance our compiler infrastructure for high-performance by using open-source technology like MLIR, LLVM, TVM and IREE.

Necessary Skills / Attributes:

• 6 to 15 years of experience in the field of compiler design and graph mapping.
• 2+ years hands-on experience with MLIR and/or LLVM.
• Experience with multiple toolchains, compilers, and Instruction Set Architectures.
• Strong knowledge of resource management, scheduling, code generation, and compute graph optimization.
• Strong expertise in writing modern standards (C++17 or newer) C++ production quality code along test-driven development principles.
• Comfortable and experienced in software development life cycle - coding, debugging, optimization, testing, and continuous integration.
• Familiarity with parallelization techniques for ML acceleration.
• Experience working on and contributing to an active compiler toolchain codebase, such as LLVM, MLIR, or Glow.
• Experience in deep learning algorithms and techniques, e.g., convolutional neural networks, recurrent networks, etc.
• Experience of developing in a mainstream machine-learning framework, e.g. PyTorch, Tensorflow or Caffe.
• Experience operating in a fast-moving environment where the workloads evolve at a rapid pace.
• Understanding of the interplay of hardware and software architectures on future algorithms, programming models and applications.
• Experience developing innovative architectures to extend the state of the art in DL performance and efficiency.
• Experience with Hardware and Software Co-design.

M.S. or higher degree, in CS/CE/EE or equivalent with industry or open-source experience.

Work Profile:

• Design, implement and test compiler features and capabilities related to infrastructure and compiler passes.
• Ingest CNN graphs in Pytorch/TF/TFLite/ONNX format and map them to hardware implementations, model data-flows, create resource utilization cost-benefit analysis and estimate silicon performance.
• Develop graph compiler optimizations (operator fusion, layout optimization, etc) that are customized to each of the different ML accelerators in the system.
• Integrate open-source and vendor compiler technology into Samsung ML internal compiler infrastructure.
• Collaborate with Samsung ML acceleration platform engineers to guide the direction of inferencing and provide requirements and feature requests for hardware vendors.
• Closely follow industry and academic developments in the ML compiler domain and provide performance guidelines and standard methodologies for other ML engineers.
• Create and optimize compiler backend to leverage the full hardware potential, efficiently optimizing them using novel approaches.
• Evaluate code performance, debug, diagnose and drive resolution of compiler and cross-disciplinary system issues.
• Contribute to the development of machine-learning libraries, intermediate representations, export formats and analysis tools.
• Communicate and collaborate effectively with cross-functional hardware and software engineering teams.
• Champion engineering and operational excellence, establishing metrics and processes for regular assessment and improvement.

Keywords to source candidates

Senior Developer, Deep Learning, Prediction engine, Machine Learning, Compiler

ML Engineer-Analyst/ Senior Analyst

Job purpose:

To design and develop machine learning and deep learning systems. Run machine learning tests andexperiments and implementing appropriate ML algorithms. Works cross-functionally with the Data Scientists, Software application developers and business groups for the development of innovative ML models. Use Agile experience to work collaboratively with other Managers/Owners in geographically distributed teams.

Accountabilities:

• Work with Data Scientists and Business Analysts to frame problems in a business context. Assist all the processes from data collection, cleaning, and preprocessing, to training models and deploying them to production.

• Understand business objectives and developing models that help to achieve them, along with metrics to track their progress.

• Explore and visualize data to gain an understanding of it, then identify differences in data distribution that could affect performance when deploying the model in the real world.

• Define validation strategies, preprocess or feature engineering to be done on a given dataset and data augmentation pipelines.

• Analyze the errors of the model and design strategies to overcome them.
• Collaborate with data engineers to build data and model pipelines, manage the infrastructure and data pipelines needed to bring code to production and demonstrate end-to-end understanding of applications (including, but not limited to, the machine learning algorithms) being created.

Qualifications & Specifications

• Bachelor's degree in Engineering /Computer Science/ Math/ Statistics or equivalent. Master's degree in relevant specification will be first preference

• Experience of machine learning algorithms and libraries
• Understanding of data structures, data modeling and software architecture.
• Deep knowledge of math, probability, statistics and algorithms
• Experience with machine learning platforms such as Microsoft Azure, Google Cloud, IBM Watson, and Amazon

• Big data environment: Hadoop, Spark
• Programming languages: Python, R, PySpark
• Supervised & Unsupervised machine learning: linear regression, logistic regression, k-means

clustering, ensemble models, random forest, svm, gradient boosting

• Sampling data: bagging & boosting, bootstrapping
• Neural networks: ANN, CNN, RNN related topics
• Deep learning: Keras, Tensorflow
• Experience with AWS Sagemaker deployment and agile methodology

Key Responsibilities:

• Partnering with clients and internal business owners (product, marketing, edit, etc.) to understand needs and develop models and products for Kaleidofin business line.
• Good understanding of the underlying business and workings of cross functional teams for successful execution
• Design and develop analyses based on business requirement needs and challenges.
• Leveraging statistical analysis on consumer research and data mining projects, including segmentation, clustering, factor analysis, multivariate regression, predictive modeling, hyperparameter tuning, ensembling etc.
• Providing statistical analysis on custom research projects and consult on A/B testing and other statistical analysis as needed. Other reports and custom analysis as required.
• Identify and use appropriate investigative and analytical technologies to interpret and verify results.
• Apply and learn a wide variety of tools and languages to achieve results
• Use best practices to develop statistical and/ or machine learning techniques to build models that address business needs.
• Collaborate with the team to improve the effectiveness of business decisions using data and machine learning/predictive modeling.
• Innovate on projects by using new modeling techniques or tools.
• Utilize effective project planning techniques to break down complex projects into tasks and ensure deadlines are kept.
• Communicate findings to team and leadership to ensure models are well understood and incorporated into business processes.

Skills:

• 2+ year experience in advanced analytics, model building, statistical modeling, optimization, and machine learning algorithms.
• Machine Learning Algorithms: Crystal clear understanding, coding, implementation, error analysis, model tuning knowledge on Linear Regression, Logistic Regression, SVM, shallow Neural Networks, clustering, Decision Trees, Random forest, Boosting trees, Recommender Systems, ARIMA and Anomaly Detection. Feature selection, hyper parameters tuning, model selection and error analysis, ensemble methods.
• Strong with programming languages like Python and data processing using SQL or equivalent and ability to experiment with newer open source tools
• Experience in normalizing data to ensure it is homogeneous and consistently formatted to enable sorting, query and analysis.
• Experience designing, developing, implementing and maintaining a database and programs to manage data analysis efforts.
• Experience with big data and cloud computing viz. Spark, Hadoop (MapReduce, PIG, HIVE)
• Experience in risk and credit scoring domains preferred

• B.Tech/MTech from tier 1 institution
• 8+years of experience in machine learning techniques like logistic regression, random forest, boosting, trees, neural networks, etc.
• Showcased experience with Python, SQL and proficiency in Scikit Learn, Pandas, NumPy, Keras and TensorFlow/pytorch
• Experience of working with Qlik sense or Tableau is a plus