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

1. Probabilistic Programming Languages (PPL)
   • Stan
   • PyMC3
   • Pyro / NumPyro
2. Probabilistic Graphical Models (PGM)
   • pgmpy
3. Causal Inference
   • doWhy
   • CausalImpact
4. Bayesian Optimisation
   • botorch
   • Ax
   • optuna
   • ray
5. Convex Optimisation
   • cvxpy
6. Gaussian Processes (GP)
   • GPyTorch
   • GPflow
7. Constrained optimisation problems
   • AMPL
   • GAMS
   • Pyomo
   • JuMP

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:

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

B) Introduction to deep learning

1. Dive into Deep Learning | Prof. Alex Smola OR
2. Practical deep learning for coders | Jeremy Howard OR
3. 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

1. Computer Vision: A Modern Approach | Forsyth, Ponce
2. Programming Computer Vision with Python | Jan Erik Solem
3. Computer Vision: Algorithms and Applications | Richard Szeliski
4. Computer Vision: Models, Learning, and Inference | Simon J. D. Prince
5. 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
  • Introduction to Computer Vision, Udacity | GeorgiaTech CS 6476 | Prof. Aaron Bobick, Prof Irfan Essa, Arpan Chakraborty
  • Swayam | Deep Learning for Computer Vision By Prof. Vineeth N Balasubramanian | IIT Hyderabad
  • CVIT Summer School series | IIIT Hyderabad

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