#### You have followed research papers on ArXiv, scored well in Kaggle competitions, implemented a few deep learning techniques. But you want to apply your knowledge to real world problems?

### Then you should consider working with us.

### Job Descriptions

#### Roles and responsibilities

- Understand business requirements and formulate solution for the problem, satisfying the time, memory and accuracy SLAs.
- Research techniques in computer science and mathematics for elegant solution.
- Code, train, evaluate and deploy models that integrate with the complete software solution.
- Own and deliver the product to the end-user, in the time alloted for the project.

#### Qualifications

- M Sc (Mathematics / Statistics) or M Tech (Computer Science) with specialization in machine learning is required
- Ph D is preferable
- Pre-requisites mentioned below
- Work-experience is preferable, but we are looking for expertise rather experience in number of years

#### Skills

- Candidate must be good at programming and be able to adapt to any of the basic programming languages like C, C++, Python, Matlab, R, Julia, Java, Go, Rust etc.
- Candidate must have mastery of basic computer science concepts like data structures, algorithms, databases, relational algebra (SQL), operating systems, computer architecture, computer networks.
- Candidate must be comfortable in programming on GNU/Linux in a high performance computing (HPC) setups like multicores, clusters, GPUs, etc.
- Candidate must be able to grasp concepts from latest research papers and implement them in a short time
- Candidate must have mastery over basics of machine learning and hands on experience with recent advances in deep learning
- Candidate must have a specialization in AI / ML and should have mastery over few of the topics in the prerequisites section
- Candidate must be familiar with ML programming frameworks and libraries and should be able to quickly learn and adapt to the newly emerging ones

#### Pre-requisites

##### Techniques

- Linear Algebra | Prof. Gilbert Strang
- Computational and Inferential Thinking: The Foundations of Data Science | Ani Adhikari, John DeNero, David Wagner
- Bayesian Data Analysis | Gelman, Carlin, Stern, Dunson, Vehtari, Rubin
- Introducing Monte Carlo Methods with R | Robert, Christian, Casella, George
- Probabilistic Programming and Bayesian Methods for Hackers | Cam Davidson Pilon
- Numerical Optimization | Nocedal and Wright
- Practical Methods of Optimization | R Fletcher
- Numerical Recipes, The Art of Scientific Computing | William H. Press, Saul A. Teukolsky, William T. Vetterling and Brian P. Flannery
- Bayesian Reasoning and Machine Learning | David Barber
- Probabilistic Graphical Models
- Causal Inference in Statistics : A Primer | Judea Pearl, Madelyn Glymour, Nicholas P. Jewell
- Causal Inference: What If | Hernán MA, Robins JM
- Elements of Causal Inference Foundations and Learning Algorithms| Bernhard Schölkopf, Dominik Janzing, and Jonas Peters
- Geometric Deep Learning | Michael Bronstein
- Graphical Models, Exponential Families and Variational Inference | Wainwright, Prof Michael I Jordan
- Variational Inference: A Review for Statisticians | David M. Blei, Alp Kucukelbir, Jon D. McAuliffe
- Information Theory, Inference and Learning Algorithms | David J.C. MacKay
- Bayesian Learning | Shakir Mohammed
- Bayesian Deep Learning and Probabilistic Model Construction | Andrew Gordon Wilson
- Statistical Learning Theory | Prof. Vladimir Vapnik
- Gaussian Processes in Machine Learning |
- Practical Time Series Forecating | Galit Shmueli, Kenneth C. Lichtendahl Jr
- Advances in Kernel Methods Support Vector Learning | Christopher J.C. Burges, Bernhard Schölkopf and Alexander J. Smola
- Semi-Supervised Learning | Olivier Chapelle, Bernhard Sch¨olkopf, Alexander Zien
- The Algorithmic Foundations of Differential Privacy | Cynthia Dwork, Aaron Roth
- Privacy Preserving Data Science Explained | OpenMined
- Privacy-Preserving Machine Learning | J. Morris Chang, Di Zhuang, and G. Dumindu Samaraweera

##### Tools

In addition to the tools mentioned in Machine Learning Engineer section, candidate should be comfortable with specilised statistical modeling tools including

- Probabilistic Programming Languages (PPL)
- Probabilistic Graphical Models (PGM)
- Causal Inference
- Bayesian Optimisation
- Convex Optimisation
- Gaussian Processes (GP)
- Constrained optimisation problems

#### Roles and responsibilities

- Code, train, evaluate and deploy machine learning models that integrate with the complete software solution.

#### Qualifications

- B Tech in Computer Science / Information Technology is required
- M Tech / PhD specialization in artificial intelligence / machine learning is preferable
- Pre-requisites mentioned below
- Work-experience is preferable, but we are looking for expertise rather experience in number of years

#### Skills

- Candidate must be good at programming and be able to adapt to any of the basic programming languages like C, C++, Python, Matlab, R, Julia, Java, Go, Rust etc.
- Candidate must have mastery of basic computer science concepts like data structures, algorithms, databases, relational algebra (SQL), operating systems, computer architecture, computer networks.
- Candidate must be comfortable in programming on GNU/Linux in a high performance computing (HPC) setups like multicores, clusters, GPUs, etc.
- Candidate must be able to grasp concepts from latest research papers and implement them in a short time
- Candidate must have a specialization in AI / ML and should have mastery over the topics in the prerequisites section
- Candidate must be familiar with ML programming frameworks and libraries and should be able to quickly learn and adapt to the newly emerging ones

#### Pre-requisites

##### Techniques

A) Undergraduate level machine learning:

- Machine Learning | Stanford University CS229 | Prof. Andrew Ng | Editions: Original / Latest / Coursera OR
- Elements of Statistical Learning: data mining, inference and prediction - Hastie, Tibshirani, Friedman | Introduction to Statistical Learning | EdX Course OR
- Swayam | Introduction to Machine Learning | Prof. Balaraman Ravindran | IIT Madras
- Machine Learning | Dr. Nando de Freitas OR
- Machine Learning: A Probabilistic Perspective | Kevin Murphy OR equivalent

B) Introduction to deep learning

- Dive into Deep Learning | Prof. Alex Smola OR
- Practical deep learning for coders | Jeremy Howard OR
- Deep Learning | Andrew Ng OR equivalent

It would be great, if you could provide a certificate of completions A (1) / (2) / (3) and B(3)

##### Tools

A machine learning engineer needs to be proficient in different aspects of computer science and engineering. Some of the tools to be familiar with include:

- Data Management
- Python, Julia, R, jupyter, Pandas, PySpark, numpy, matplotlib, seaborn, streamlit, Kafka

- Core Computer Science
- C, C++, Python,Java, Scala, NetworkX, igraph, MySQL, PostgreSQL, Linux, Mac, Windows

- Machine Learning
- PyTorch, Tensorflow, Keras, scikit-learn, XGBoost, LightGBM

- Artificial Intelligence
- OpenCV, dlib. scikit-image, nltk, SpaCy, faiss, flann, kaldi, sphinx, librosa

- Systems / Computing
- OpenMP, MPI, Spark, CUDA, AWS, GCloud, Azure, Mosquitto, Paho, Jetson Nano

- Software Engineering
- Docker, Git, JIRA, Trello, MLOps toolkits

#### Roles and responsibilities

- Code, train, evaluate and deploy machine learning models that integrate with the complete software solution.

#### Qualifications

- B Tech in Computer Science / Information Technology
- M Tech / PhD specialization in computer vision

#### Skills

- Candidate must be good at programming and be able to adapt to any of the basic programming languages like C, C++, Python, Matlab, R, Julia, Java, Go, Rust etc.
- Candidate must have mastery of basic computer science concepts like data structures, algorithms, databases, relational algebra (SQL), operating systems, computer architecture, computer networks.
- Candidate must be comfortable in programming on GNU/Linux in a high performance computing (HPC) setups like multicores, clusters, GPUs, etc.
- Candidate must be able to grasp concepts from latest research papers and implement them in a short time.
- Candidate must have a specialization in AI / ML and should have mastery over the topics in the prerequisites section.
- Candidate must be familiar with ML programming frameworks and libraries and should be able to quickly learn and adapt to the newly emerging ones.
- Candidate must be familiar with computer vision libraries, frameworks, toolboxes and should be able to quickly learn and adapt to the emerging ones.

#### Pre-requisites

##### Techniques

A) Computer Vision

- Computer Vision: A Modern Approach | Forsyth, Ponce
- Programming Computer Vision with Python | Jan Erik Solem
- Computer Vision: Algorithms and Applications | Richard Szeliski
- Computer Vision: Models, Learning, and Inference | Simon J. D. Prince
- Multiple View Geometry in Computer Vision | Hartley, Zisserman

B) Deep learning for computer vision

Pre-requisites for Machine Learning Engineer.

- Following certifications will be preferred

##### Tools

A computer vision specialist needs to be proficient in different aspects of computer science and engineering. Some of the tools to be familiar with include:

- Data Management
- Python, Julia, R, jupyter, Pandas, PySpark, numpy, matplotlib, seaborn, streamlit, Kafka

- Core Computer Science
- C, C++, Python,Java, Scala, NetworkX, igraph, MySQL, PostgreSQL, Linux, Mac, Windows

- Machine Learning
- PyTorch, Tensorflow, Keras, scikit-learn, XGBoost, LightGBM

- Artificial Intelligence
- OpenCV, dlib. scikit-image, nltk, SpaCy, faiss, flann, kaldi, sphinx, librosa

- Systems / Computing
- OpenMP, MPI, Spark, CUDA, AWS, GCloud, Azure, Mosquitto, Paho, Jetson Nano

- Software Engineering
- Docker, Git, JIRA, Trello, MLOps toolkits

## INVERVIEW PROCESS

#### Round 1 : Screening with MCQ

A timed 20 minute MCQ test on machine learning

#### Round 2: Programming Assignement 1 [48 hour]

- Short programming assignment to evaluate programming skills and understanding of basics of deep learning
- Meeting for code walkthrough

#### Round 3: Programming Assignment 2 [3 week]

- This programming assignment will challenge the candidate on solving real problems on large datasets (unlike toy datasets like MNIST which are used in most tutorial examples). If the candidate has a specific area of interest like computer vision, natural language processing, speech processing, recommender systems etc do let us know. We can set up the assignment accordingly.
- Meeting for code walkthrough

#### Round 4: Technical Interview [1 hour]

Questions to gauge understanding of machine learning

#### Round 5 : HR discussion

Discussion with HR

Note: Round 1 and Round 2 are exempted for candidates:

- M Tech / PhD from IITs, IISc, IIITs or top-100 universities
- Kaggle Masters and Grandmasters
- Publications at one of the following conferences / publications : vision, AI,data mining, NLP, stats, optimization

## Connect With Us

**Dataeaze Systems Private Limited **

Plot no 41, Lane no 3, Shivaji Housing Society, Senapati Bapat Rd, Shivajinagar, Pune, Maharashtra 411016.