Senior Machine Learning Engineer - Arbisoft

Senior Machine Learning Engineer – Arbisoft

Asad Hameed

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Topics we cover!
1 Responsibilities
2 Qualifications
3 Benefits
4 How to Apply
5 FAQs

Arbisoft is looking for a Senior Machine Learning Engineer to join their team in Lahore, Punjab, Pakistan. If you are passionate about leveraging machine learning to create impactful solutions, this is the perfect opportunity for you. The ideal candidate will bring a strong background in Python programming, data science, and machine learning, with specific experience in financial market data and cloud technologies. If you’re ready to be part of an innovative and dynamic team that pushes the boundaries of technology, we want to hear from you!

Responsibilities

As a Senior Machine Learning Engineer at Arbisoft, you will be responsible for building and optimizing machine learning models, processing big data, and developing innovative solutions. Here’s a detailed breakdown of your role:

  1. Machine Learning and Data Science:
    • Utilize Python and popular machine learning libraries (such as pandas, NumPy, statsmodels, sklearn, and TensorFlow) to develop and optimize algorithms.
    • Focus on time series analysis, including techniques like ARIMA, time series decomposition, and multivariate analysis to make accurate predictions from financial data.
    • Apply advanced machine learning techniques such as SVD/PCA, LSTM, and NeuralProphet for more sophisticated insights.
    • Experiment with ensemble methods like Xgboost and LightGBM to improve model performance.
    • Work with Spark and PySpark to handle big data processing efficiently.
  2. Financial Market Data:
    • Analyze and work with financial market data, contributing your expertise to derive meaningful insights from complex datasets.
    • Use your deep understanding of financial analytics to build models that help in predictive analysis, risk assessment, and market trends.
  3. DevOps and Cloud Services:
    • Manage cloud-based deployments and environments, mainly using Amazon AWS.
    • Work with DevOps tools like Docker for containerization and Kubernetes for orchestration.
    • Build microservices using Flask and FastAPI and deploy them on platforms like AWS Sagemaker.
  4. Technical Proficiency:
    • Maintain strong SQL skills to handle and query large datasets in relational databases such as PostgreSQL or Snowflake.
    • Understand and implement object-oriented programming (OOP) principles in Python to ensure well-structured and maintainable code.
    • Use Git for version control, ensuring proper code management across development cycles.
  5. Advanced Skills:
    • Familiarity with machine learning optimization techniques such as CUDA, Triton, TensorRT, and ONNX to improve model efficiency.
    • Use AWS Step Functions and AWS Batch for workflow orchestration and job scheduling.
    • Experience with caching technologies like Redis and MemCache to optimize performance.

Qualifications

The ideal candidate will meet the following qualifications:

  1. Experience:
    • At least 5 years of hands-on experience in machine learning and data science, with proficiency in Python and machine learning libraries.
    • Strong background in time series analysis and financial market data analytics.
    • Extensive experience in DevOps, cloud technologies (especially AWS), and containerization with Docker and Kubernetes.
  2. Technical Skills:
    • Deep knowledge of machine learning algorithms (e.g., ARIMA, LSTM, Xgboost, etc.) and data processing frameworks like Spark.
    • Proficiency in SQL and relational databases (PostgreSQL, Snowflake) to query and manage data.
    • Experience with PyTorch, CUDA GPU optimizations, and machine learning model deployment is a plus.
    • Familiarity with Bayesian Hyperparameter Optimization (HPO) and job scheduling technologies like AWS Step Functions.
  3. Preferred Education:
    • A Master’s or Ph.D. in machine learning, data engineering, or a related field is preferred but not required. Equivalent experience will also be considered.
  4. Additional Skills:
    • Strong problem-solving and critical-thinking abilities.
    • Excellent communication and teamwork skills, as you will collaborate with a wide range of teams across Arbisoft.

Benefits

At Arbisoft, we are committed to providing an excellent working environment and offering valuable benefits to our employees. Some of the benefits include:

  1. Competitive Salary: Arbisoft offers a competitive salary based on your skills and experience, ensuring that your contributions are well-rewarded.
  2. Career Growth: We foster an environment of continuous learning and development. As a Senior Machine Learning Engineer, you will have opportunities to work on cutting-edge projects and grow your skills.
  3. Hybrid Work Environment: You will have the flexibility to work in a hybrid setup, with some days in the office and others working remotely.
  4. Health and Well-being: We offer comprehensive health benefits to ensure that you stay healthy and focused while working at Arbisoft.
  5. Innovative Projects: You will work on innovative projects in the fields of machine learning and financial analytics, contributing to high-impact solutions for our clients.
  6. Collaboration: Arbisoft values teamwork and collaboration. You’ll work alongside talented engineers, data scientists, and other professionals, making a significant impact on the company’s success.
  7. Work-Life Balance: We understand the importance of maintaining a healthy work-life balance and offer a flexible work schedule to support it.

How to Apply

To apply for the Senior Machine Learning Engineer position at Arbisoft, follow these steps:

  1. Prepare Your Resume: Make sure your resume highlights your relevant experience, skills, and achievements in machine learning, data science, and DevOps. Be sure to include specific projects and technologies you have worked with.
  2. Submit Your Application: Apply through the job portal or send your updated resume to careers@arbisoft.com. Be sure to include a cover letter that outlines your experience and motivation for applying.
  3. Interview Process: If selected, you will be invited for an interview where you’ll discuss your skills, previous experience, and how you can contribute to the success of Arbisoft. Be prepared to discuss technical challenges and problem-solving approaches in machine learning.
  4. Join Our Team: If you are selected, you will join a talented team of engineers and data scientists, working on cutting-edge technologies and helping to shape the future of machine learning.

FAQs

Q1: What is the work environment like at Arbisoft?

Arbisoft offers a hybrid work environment, giving employees the flexibility to work both remotely and on-site. This setup allows for a healthy work-life balance.

Q2: Is experience in financial market data required for this role?

Yes, at least 5 years of experience with financial market data and financial analytics is required for this role. Your ability to analyze and draw insights from complex financial datasets will be a key part of the role.

Q3: Do I need a Master’s or Ph.D. to apply?

While a Master’s or Ph.D. is preferred, it is not mandatory. Equivalent experience in the field of machine learning and data science will be considered.

Q4: What are the main tools and technologies I will use in this role?

You will work with Python, TensorFlow, Spark, PySpark, AWS, Docker, Kubernetes, SQL, and machine learning optimization tools such as CUDA and Triton.

Q5: How can I demonstrate my experience during the interview?

During the interview, be prepared to discuss specific machine learning projects you’ve worked on, detailing the challenges faced, the solutions you implemented, and the results achieved. Having a strong portfolio or examples of your work can also help.

Asad Hameed
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