Senior Machine Learning Engineer Expert

Job Description

Job Description 

We are seeking a motivated and enthusiastic expert in domain of Applied AI-ML oversee and drive machine learning initiatives focusing on time series analysis, process curve analysis, tabular data, and feature engineering. The ideal candidate will have a foundational understanding of time series concepts, process curve use-cases and experience with relevant tools and technologies. As a AI-ML Expert, you will work alongside senior engineers and researchers to 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 high-quality solutions on time.

Roles & Responsibilities:
Machine Learning and Data Engineering:

1. Time Series Analysis: Develop and implement advanced machine learning models for analyzing time-series data (e.g., forecasting, anomaly detection).

2. Tabular Data: Manage and work with structured/tabular datasets to build models that deliver actionable insights.

3. Feature Engineering: Design and implement innovative feature engineering techniques to enhance model performance, ensuring that features align with business goals.

4. Model Development and Optimization: Develop, test, and optimize machine learning models and algorithms for various business use cases.

5. Deep-Learning -

6. LLM & RAG

7. Agentic-AI Frameworks

Leadership and Team Management:

1. Team Mentorship: Lead a team of machine learning engineers and data scientists, providing guidance and mentorship to junior team members.

2. Collaboration: Work closely with data scientists, software engineers, product managers, and other stakeholders to design, implement, and deliver end-to-end solutions.

3. 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.

4. Client Deliverables: Ensure all project milestones are met, and machine learning models and solutions are aligned with customer expectations.

Pipeline and Workflow Design:

1. CI/CD Pipeline: Design and maintain robust CI/CD pipelines for machine learning model training, validation, and deployment, ensuring efficient and automated workflows.

2. Model Deployment and Monitoring: Oversee the deployment of machine learning models into production, ensuring they meet performance, reliability, and scalability requirements.

3. Automated Workflows: Build automated workflows for data pipelines, model training, evaluation, and reporting, ensuring seamless integration with business processes.

Quality Assurance and Optimization:

1. Performance Monitoring: Monitor model performance post-deployment, identifying and addressing any issues related to accuracy, speed, or scalability.

2. Process Improvement: Continuously evaluate and improve model development practices, machine learning pipelines, and workflows to drive efficiency and reduce time-to-market.

3. Documentation: Ensure that all models, pipelines, and processes are well-documented and easily reproducible for future iterations or modifications.
    

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+ total with 8+ years of experience in machine learning engineering with a focus on time-series analysis, process curve analysis, tabular data, and feature engineering.

• 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

Mandatory/requires 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.