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Machine Learning Engineer - Financial AI

This role is for one of the Weekday's clients

Salary range: Rs 8000000 - Rs 14000000 (ie INR 80-140 LPA)

Min Experience: 2 years

Location: India

JobType: full-time

Join us on a mission to revolutionize the financial industry with cutting-edge AI. As a Machine Learning Engineer, you’ll play a central role in developing advanced models that make sense of complex financial data—structured, unstructured, textual, tabular, and visual. You'll work at the intersection of machine learning, natural language processing, and computer vision to build intelligent systems that deliver real impact.

What You’ll Do

  • Design, develop, and deploy state-of-the-art ML models for:
    • Multi-modal Information Extraction: Uncover key insights from diverse data formats like text, tables, and charts.
    • Term Disambiguation: Identify semantically equivalent financial terms expressed in different ways.
    • Financial Machine Translation: Translate financial statements across languages with high fidelity.
  • Collaborate with software engineers, data scientists, and finance experts to scope projects and integrate models into production environments.
  • Build robust data pipelines for preprocessing, model training, evaluation, and deployment.
  • Conduct rigorous experimentation and benchmarking to ensure model accuracy and reliability.
  • Monitor and maintain live ML systems, proactively addressing performance issues.
  • Contribute to innovation by staying up to date with emerging AI/ML research and sharing knowledge across the team.

What We’re Looking For

  • Master’s degree in Computer Science, Data Science, or related field—or equivalent industry experience.
  • Strong programming skills in Python and experience with Git and version control workflows.
  • Solid grasp of machine learning and deep learning fundamentals.
  • Hands-on experience with Natural Language Processing, including:
    • Text preprocessing, regex, tokenization, stemming, lemmatization
    • Transformer models and large language models (LLMs)
  • Familiarity with Computer Vision and basic image processing techniques.
  • Proficient with ML libraries and frameworks such as PyTorch, HuggingFace, OpenCV, scikit-learn, NumPy, spaCy, NLTK.
  • Ability to explain complex technical concepts clearly to technical and non-technical stakeholders.
  • Strong organizational skills and experience managing multiple projects independently.
  • Experience in ML research in areas like representation learning, information retrieval, or object detection.
  • Self-motivated with a passion for continuous learning and innovation in AI.

Bonus Skills

  • Experience with Django or backend development.
  • Exposure to LLMs, vector databases, and frameworks like LangChain.
  • Familiarity with deploying NLP/ML models on cloud platforms such as AWS, Azure, or GCP.

Average salary estimate

$13200 / YEARLY (est.)
min
max
$9600K
$16800K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Machine Learning Engineer - Financial AI, Weekday AI

Join us at Weekday as a Machine Learning Engineer - Financial AI, and be part of a groundbreaking journey to transform the financial industry through advanced AI technologies. In this role, you will leverage your expertise to design, develop, and deploy cutting-edge machine learning models that interpret and analyze complex financial data, whether it's structured, unstructured, textual, tabular, or visual. Collaborating with a talented team of software engineers, data scientists, and finance experts, you’ll work on exciting projects like multi-modal information extraction and financial machine translation, ensuring that your models are not just effective but also integrated seamlessly into production. You'll be responsible for building robust data pipelines and conducting thorough evaluations to guarantee model accuracy. Your contributions will extend to monitoring live ML systems, addressing any performance issues proactively, and keeping pace with the latest AI/ML research trends to foster innovation within the team. To excel in this position, a Master’s degree in Computer Science, Data Science, or equivalent practical experience is essential. Strong programming skills in Python, familiarity with machine learning and deep learning fundamentals, and hands-on experience in Natural Language Processing are all key qualifications. Join us today and help shape the future of finance with intelligent systems that truly make a difference!

Frequently Asked Questions (FAQs) for Machine Learning Engineer - Financial AI Role at Weekday AI
What are the main responsibilities of the Machine Learning Engineer - Financial AI at Weekday?

As a Machine Learning Engineer - Financial AI at Weekday, your main responsibilities will involve designing, developing, and deploying advanced machine learning models aimed at extracting insights from various types of financial data. You will collaborate with software engineers and finance professionals to integrate these models into production environments while building efficient data pipelines, conducting experimentation, and monitoring live systems to maintain performance and accuracy.

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What qualifications are necessary for the Machine Learning Engineer - Financial AI position at Weekday?

To qualify for the Machine Learning Engineer - Financial AI role at Weekday, candidates must possess a Master’s degree in Computer Science, Data Science, or a related field, or have equivalent practical experience. Additionally, strong programming skills in Python, a solid understanding of machine learning fundamentals, and hands-on experience with Natural Language Processing and computer vision techniques are crucial for success in this position.

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How can experience with Natural Language Processing benefit a Machine Learning Engineer - Financial AI at Weekday?

Experience with Natural Language Processing (NLP) can greatly enhance your effectiveness as a Machine Learning Engineer - Financial AI at Weekday. This skill will enable you to manage tasks such as text preprocessing, term disambiguation, and even financial machine translation, allowing you to handle and analyze complex financial documents and communications, ultimately delivering high-impact insights.

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What programming languages and tools are required for the Machine Learning Engineer - Financial AI role at Weekday?

For the Machine Learning Engineer - Financial AI position at Weekday, proficiency in Python is essential, alongside familiarity with version control systems like Git. Practical experience using ML libraries and frameworks such as PyTorch, HuggingFace, OpenCV, and scikit-learn will be highly beneficial in successfully executing your responsibilities.

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What can candidates expect in terms of career growth as a Machine Learning Engineer - Financial AI at Weekday?

As a Machine Learning Engineer - Financial AI at Weekday, candidates can look forward to significant career growth. The role provides exposure to innovative projects, the opportunity to collaborate with industry experts, and access to emerging AI/ML research. This environment fosters continuous learning and innovation, setting the stage for professional development and advancement in the financial technology landscape.

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Common Interview Questions for Machine Learning Engineer - Financial AI
Can you explain a machine learning model you have developed and its impact?

When answering this question, focus on a specific model, the problem it addressed, the data used, the methodologies implemented, and the overall impact of the model on business outcomes or processes. Highlight how you measured success and any challenges you faced during development.

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How do you handle data preprocessing in machine learning projects?

Discuss your approach to data preprocessing, including techniques like normalization, handling missing values, and feature extraction. Emphasize the importance of data quality in building reliable machine learning models and any tools or libraries you frequently use.

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What are some challenges you have faced while working with Natural Language Processing?

Be prepared to discuss specific challenges such as ambiguity in language, the variety of dialects and syntax, and the limitations of certain NLP models. Explain how you managed these challenges and what solutions you implemented.

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How do you evaluate the effectiveness of a machine learning model?

Explain the performance metrics you use to evaluate models, such as precision, recall, F1 score, and ROC-AUC, and discuss the importance of validation techniques like cross-validation. Include how you adjust models based on evaluation results.

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Can you describe your experience with deep learning frameworks?

Talk about your familiarity with frameworks like TensorFlow, PyTorch, or Keras, and what types of projects you've used them for. Highlight any specific architectures you’ve worked with and how they contributed to the project's success.

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What steps do you take to keep up with advancements in AI/ML research?

Share how you follow academic journals, attend conferences, participate in online courses, or engage in community discussions. This demonstrates your commitment to continuous learning and staying updated with the fast-paced AI/ML field.

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Explain your understanding of transformer models and their applications.

Provide a concise explanation of transformer models, their architecture, and their use cases in NLP tasks like machine translation and sentiment analysis. Highlight any projects you’ve been involved in that utilized these models.

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How do you prioritize and manage multiple projects simultaneously?

Discuss your organizational skills, the tools you use for project management, and how you set priorities based on deadlines and project impact. Give examples of how you've effectively balanced competing responsibilities.

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What is your approach to collaborating with non-technical stakeholders?

Describe how you tailor your communication to suit non-technical stakeholders by breaking down complex concepts into simpler terms and using visuals where applicable. Emphasize your ability to gather requirements and ensure all parties are aligned.

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How would you tackle a performance issue in a live ML system?

Outline steps for diagnosing the performance issue, including monitoring system performance metrics, reviewing model inputs, and considering potential data drift. Discuss the process of making incremental improvements to the model while maintaining system reliability.

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EMPLOYMENT TYPE
Full-time, remote
DATE POSTED
April 15, 2025

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