Senior ML Engineer Lead

Looking for a highly skilled Senior Machine Learning Engineering Lead to oversee and drive machine learning initiatives focusing on time series analysis process curve analysis tabular data and feature engineering. In this role you will lead a team of engineers and data scientists ensuring the effective delivery of machine learning solutions to customers. You will also be responsible for designing efficient workflows building robust CI/CD pipelines and handling client interactions to deliver highquality solutions on time.Roles & Responsibilities:

Machine Learning and Data Engineering:

Time Series Analysis: Develop and implement advanced machine learning models for analyzing timeseries 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 endtoend solutions.

Customer Handling: Serve as the primary point of contact for customers gathering requirements addressing technical challenges and ensuring the timely delivery of highquality 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 postdeployment 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 timetomarket.

Documentation: Ensure that all models pipelines and processes are welldocumented 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 scikitlearn TensorFlow Keras XGBoost PyTorch etc.

Time Series Analysis: Experience with timeseries forecasting models (ARIMA LSTM Prophet etc.) and anomaly detection.

Data Engineering: Expertise in working with largescale 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 nontechnical stakeholders and delivering presentations.

Problemsolving mindset with the ability to analyze complex data and identify actionable insights.

Highly organized detailoriented and able to manage multiple projects simultaneously.

Experience:

8 years of experience in machine learning engineering with a focus on timeseries analysis process curve analysis tabular data and feature engineering.

At least 35 years of leadership experience managing teams and handling customerfacing responsibilities.

Strong experience in designing and deploying ML models in production environments.

Proven track record of successfully managing client relationships and delivering highquality solutions on time.

Experienced in working in crossfunctional international setups

Entrepreneurial businessdriven mindset.

Preferred Expertise:

Experience in deploying models at scale using containerization technologies like Docker and Kubernetes.

Knowledge of MLOps principles and practices.

Background in domainspecific areas (e.g. manufacturing finance healthcare) related to timeseries and process data.

Qualifications :

B.E./ M.E./M. Tech in Computer Science Engineering Ph. D is plus

Additional Information :

8 12 years

Remote Work :

No

Employment Type :

Fulltime