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Python
R ProgrammingDay in the life...
As a machine learning engineer at Neysa, you would be required to
- Collaborate with network engineers and IT teams to identify network-related challenges and areas where ML can provide solutions. Understand the specific network remediation problems that need to be addressed.
- Develop ML-based models and algorithms specific to issues that affect computer networks, for example, congestion, security threats and human errors.
- Be comfortable handling multiple types and data sources and then pre-process and clean them for modelling purposes.
- Develop machine learning models that analyse networking data to predict (and possibly prevent) issues, detect anomalies or optimise performance. Choose the right approach for each, such as deep learning, reinforcement learning, or traditional statistical methods.
- Train and evaluate the efficacy of your models, create performance metrics to assess robustness and effectiveness, and use feedback loops to make course corrections.
- Design solutions that can scale to handle large network environments efficiently. This typically means optimising execution latency and resource usage.
- Integrate your model to work with running and maintaining a network. These could be with other machines, human operators, or both.
- Document your design, architecture and your thought process. Work with the technical writers to make sure your message gets through.
- Stay updated with all that is changing in AI and ML.
Must have skills
- You should have expertise in machine learning algorithms, data processing, and model development. It would be best to have proficiency in associated frameworks such as TensorFlow, PyTorch or scikit-learn.
- You should understand how computer networks work, how they are used, how they fail, and what happens if they fail.
- You should be proficient in programming languages like Python, R, Go, LISP, etc. It would help if you also were very useful with old-school shell scripting.
- Experience with data processing tools like Hadoop, Spark or Kafka.
- An above-average understanding of Linux and operating systems in general.
What separates the best from the rest
ML and AI are continuously evolving, and so is understanding how these technologies can be applied to solve real-world problems. To do your best, you may also need to
- Conceptualise ways to map new problems to existing methodologies or create new ones.
- Be prepared to iterate, reiterate and then iterate your approach.
- Be able to interact with subject matter experts in multiple fields to identify potential use cases for machine learning.
What you can expect
An environment where you can do your best work...
- The best equipment that complements your talents
- The best tools in the business for you to bring your creations to life
- A great environment
- Flexible work hours and flexible work locations
- The opportunity to make your mark and shape the future
- And have fun...
Data Science
Machine Learning (ML)About UpSolve
We built and deliver complex AI solutions which help drive business decisions faster and more accurately. We are a typical AI company and have a range of solutions developed on Video, Image and Text.
What you will do
- Stay informed on new technologies and implement cautiously
- Maintain necessary documentation for the project
- Fix the issues reported by application users
- Plan, build, and design solutions with a mental note of future requirements
- Coordinate with the development team to manage fixes, code changes, and merging
Location: Mumbai
Working Mode: Remote
What are we looking for
- Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.
- Minimum 2 years of professional experience in software development, with a focus on machine learning and full stack development.
- Strong proficiency in Python programming language and its machine learning libraries such as TensorFlow, PyTorch, or scikit-learn.
- Experience in developing and deploying machine learning models in production environments.
- Proficiency in web development technologies including HTML, CSS, JavaScript, and front-end frameworks such as React, Angular, or Vue.js.
- Experience in designing and developing RESTful APIs and backend services using frameworks like Flask or Django.
- Knowledge of databases and SQL for data storage and retrieval.
- Familiarity with version control systems such as Git.
- Strong problem-solving and analytical skills.
- Excellent communication and collaboration abilities.
- Ability to work effectively in a fast-paced and dynamic team environment.
- Good to have Cloud Exposure
India's first Options Trading Analytics platform on mobile.
Location : Andheri East
Notice Period: Immediate-15 days
Responsibilities:
1. Study and transform data science prototypes.
2. Design NLP applications.
3. Select appropriate annotated datasets for Supervised Learning methods.
4. Find and implement the right algorithms and tools for NLP tasks.
5. Develop NLP systems according to requirements.
6. Train the developed model and run evaluation experiments.
7. Perform statistical analysis of results and refine models.
8. Extend ML libraries and frameworks to apply in NLP tasks.
9. Use effective text representations to transform natural language into useful features.
10. Develop APIs to deliver deciphered results, optimized for time and memory.
Requirements:
1. Proven experience as an NLP Engineer of at least one year.
2. Understanding of NLP techniques for text representation, semantic extraction techniques, data
structures and modeling.
3. Ability to effectively design software architecture.
4. Deep understanding of text representation techniques (such as n-grams, bag of words, sentiment
analysis etc), statistics and classification algorithms.
5. Hands on Experience of Knowledge of Python of more than a year.
6. Ability to write robust and testable code.
7. Experience with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)
8. Strong communication skills.
9. An analytical mind with problem-solving abilities.
10. Bachelor Degree in Computer Science, Mathematics, Computational Linguistics.
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