Machine Learning and Data Engineering:
Time Series Analysis: Develop and implement advanced machine learning models for analyzing time-series data (e.g., forecasting, anomaly detection).
Process Curve Analysis: Apply machine learning techniques to analyze process curves, optimize processes, and predict system behavior based on historical data.
Tabular Data: Manage and work with structured/tabular datasets to build models that deliver actionable insights.
Feature Engineering: Design and implement innovative feature engineering techniques to enhance model performance, ensuring that features align with business goals.
Model Development and Optimization: Develop, test, and optimize machine learning models and algorithms for various business use cases.
Leadership and Team Management:
Team Mentorship: Lead a team of machine learning engineers and data scientists, providing guidance and mentorship to junior team members.
Collaboration: Work closely with data scientists, software engineers, product managers, and other stakeholders to design, implement, and deliver end-to-end solutions.
Customer Handling: Serve as the primary point of contact for customers, gathering requirements, addressing technical challenges, and ensuring the timely delivery of high-quality solutions.
Client Deliverables: Ensure all project milestones are met, and machine learning models and solutions are aligned with customer expectations.
Pipeline and Workflow Design:
CI/CD Pipeline: Design and maintain robust CI/CD pipelines for machine learning model training, validation, and deployment, ensuring efficient and automated workflows.
Model Deployment and Monitoring: Oversee the deployment of machine learning models into production, ensuring they meet performance, reliability, and scalability requirements.
Automated Workflows: Build automated workflows for data pipelines, model training, evaluation, and reporting, ensuring seamless integration with business processes.Quality Assurance and Optimization:
Performance Monitoring: Monitor model performance post-deployment, identifying and addressing any issues related to accuracy, speed, or scalability.
Process Improvement: Continuously evaluate and improve model development practices, machine learning pipelines, and workflows to drive efficiency and reduce time-to-market.
Documentation: Ensure that all models, pipelines, and processes are well-documented and easily reproducible for future iterations or modifications.
Required skills:Technical Skills:
Programming Languages: Proficiency in Python, R, or other relevant languages (e.g., Java, Scala).
Machine Learning Frameworks: Expertise in ML libraries like scikit-learn, TensorFlow, Keras, XGBoost, PyTorch, etc.
Time Series Analysis: Experience with time-series forecasting models (ARIMA, LSTM, Prophet, etc.) and anomaly detection.
Data Engineering: Expertise in working with large-scale datasets and tools like Pandas, NumPy, SQL, and data wrangling techniques.
Feature Engineering: Strong skills in creating meaningful features to improve model accuracy and performance.
CI/CD Tools: Experience with CI/CD tools like Jenkins, GitLab, CircleCI, or similar platforms for automating deployment workflows.
Cloud Platforms: Experience with cloud computing services like AWS, GCP, or Azure for model deployment and scalability.
Version Control: Proficient in using Git for version control and collaboration.
Soft Skills:
- Strong leadership and team management skills, with a focus on mentoring and development of team members.
- Excellent communication skills for handling customer interactions, explaining technical concepts to non-technical stakeholders, and delivering presentations.
- Problem-solving mindset with the ability to analyze complex data and identify actionable insights.
- Highly organized, detail-oriented, and able to manage multiple projects simultaneously.
Experience:
- 8+ years of experience in machine learning engineering with a focus on time-series analysis, process curve analysis, tabular data, and feature engineering.
- At least 3-5 years of leadership experience managing teams and handling customer-facing responsibilities.
- Strong experience in designing and deploying ML models in production environments.
- Proven track record of successfully managing client relationships and delivering high-quality solutions on time.
- Experienced in working in cross-functional, international setups
- Entrepreneurial, business-driven mindset.
Preferred Expertise:
- Experience in deploying models at scale using containerization technologies like Docker and Kubernetes.
- Knowledge of MLOps principles and practices.
- Background in domain-specific areas (e.g., manufacturing, finance, healthcare) related to time-series and process data.
Qualifications
B.E./ M.E./M. Tech in Computer Science Engineering, Ph. D is plus