Job Summary

We are seeking a motivated and enthusiastic AI-ML Expert to oversee and drive machine learning initiatives focused on time series analysis, process curve analysis, tabular data, and feature engineering. The ideal candidate will have a foundational understanding of time series concepts and experience with relevant tools and technologies. In this role, you will work alongside senior engineers and researchers, ensuring the effective delivery of machine learning solutions to customers. You will also be responsible for designing efficient workflows, building robust Continuous Integration/Continuous Deployment (CI/CD) pipelines, and managing client interactions to deliver high-quality solutions on time.

Responsibilities

• Machine Learning and Data Engineering:

  • Develop and implement advanced machine learning models for analyzing time-series data (e.g., forecasting, anomaly detection).
  • Manage and work with structured/tabular datasets to build models that deliver actionable insights.
  • Design and implement innovative feature engineering techniques to enhance model performance.
  • Develop, test, and optimize machine learning models and algorithms for various business use cases.

• Deep Learning:

  • Work on large language models (LLM) and Retrieval-Augmented Generation (RAG).
  • Implement agentic-AI frameworks.

• Leadership and Team Management:

  • Lead a team of machine learning engineers and data scientists, providing guidance and mentorship to junior members.
  • Collaborate with data scientists, software engineers, product managers, and other stakeholders to design, implement, and deliver end-to-end solutions.
  • Serve as the primary point of contact for customers, addressing technical challenges, and ensuring timely delivery of high-quality solutions.
  • Ensure all project milestones are met, aligning solutions with customer expectations.

• Pipeline and Workflow Design:

  • Design and maintain robust CI/CD pipelines for machine learning model training, validation, and deployment.
  • Oversee the deployment of machine learning models into production, ensuring they meet performance, reliability, and scalability requirements.
  • Build automated workflows for data pipelines, model training, evaluation, and reporting.

• Quality Assurance and Optimization:

  • Monitor model performance post-deployment, identifying and addressing any issues related to accuracy, speed, or scalability.
  • Continuously evaluate and improve model development practices and workflows to drive efficiency.
  • Ensure that all models, pipelines, and processes are well-documented for future iterations.

Qualifications

• Educational Qualification:

  • Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field (Ph.D. is a plus).

• Experience:

  • 10+ years of total experience with 8+ years in machine learning engineering focusing on time-series analysis, process curve analysis, tabular data, and feature engineering.
  • Strong experience in designing and deploying machine learning models in production environments.
  • Proven track record of managing client relationships and delivering high-quality solutions on time.
  • Experience working in cross-functional, international teams.
  • Entrepreneurial, business-driven mindset.

• Mandatory Skills:

  • Proficiency in programming languages such as Python, R, or Java/Scala.
  • Expertise in machine learning frameworks like scikit-learn, TensorFlow, Keras, XGBoost, PyTorch, etc.
  • Experience with time-series forecasting models (ARIMA, LSTM, Prophet, etc.) and anomaly detection.
  • Proficiency with large-scale datasets and tools like Pandas, NumPy, SQL, and data wrangling techniques.
  • Strong skills in feature engineering to improve model accuracy and performance.
  • Familiarity with CI/CD tools such as Jenkins, GitLab, CircleCI, or similar platforms.

Preferred Skills

• Experience with specific machine learning libraries and tools beneficial for the role but not mandatory.

Experience

• Minimum 10 years of total experience with at least 8 years focused on machine learning engineering related to time series and tabular data.