Senior Machine Learning Engineer

Location: Karachi, Pakistan
Salary Range: PKR 300,000 – 500,000 per month (depending on experience)

Position Summary:

We are looking for a talented and experienced Senior Machine Learning Engineer with 4+ years of hands-on experience to join our growing AI team. In this role, you will be responsible for designing, developing, and deploying machine learning models and algorithms to solve real-world business problems. You will lead the creation of AI-powered solutions in areas such as predictive analytics, recommendation systems, computer vision, and natural language processing (NLP).

As a Senior ML Engineer, you will collaborate with data scientists, engineers, and business teams to deliver scalable, reliable, and high-performance machine learning models that enhance decision-making and improve business outcomes.

Key Responsibilities:

1. Model Development & Deployment:
   Lead the design, development, and deployment of machine learning models for various applications, including predictive modeling, recommendation systems, NLP, and computer vision. Utilize advanced machine learning algorithms (e.g., decision trees, SVM, ensemble methods, deep learning, reinforcement learning) to create scalable and efficient solutions. Implement models into production-ready systems, ensuring that they meet the scalability, performance, and security requirements.
2. Data Preprocessing & Feature Engineering:
   Oversee data collection, cleaning, preprocessing, and transformation to ensure data quality and consistency for machine learning models. Develop and implement effective feature engineering techniques to extract meaningful information from raw data.
3. Model Optimization & Performance Tuning:
   Continuously experiment with and optimize machine learning models to improve accuracy, performance, and efficiency. Conduct hyperparameter tuning, model validation, and cross-validation to ensure the robustness of the models. Ensure that models are optimized for both speed and performance, and are capable of handling large-scale datasets.
4. Machine Learning Algorithms & Research:
   Stay up to date with the latest advancements in machine learning, deep learning, and artificial intelligence, and incorporate new techniques into the development process. Conduct research on novel machine learning algorithms and evaluate their potential application to the company's business challenges.
5. Collaborate Across Teams:
   Work closely with data engineers to ensure seamless data pipeline integration for training and serving machine learning models. Collaborate with product managers and business teams to understand use cases and tailor ML solutions to meet business needs. Mentor and guide junior team members, helping them to enhance their technical skills and understanding of machine learning principles.
6. Model Monitoring & Maintenance:
   Monitor the performance of deployed models and implement continuous monitoring systems to track model drift, accuracy, and performance over time. Implement solutions for model retraining, scaling, and optimization to ensure models remain effective in production environments.
7. Documentation & Reporting:
   Write clear documentation for machine learning models, including descriptions, methods, results, and performance metrics. Provide regular updates and reports to stakeholders, communicating the impact of machine learning models on business outcomes.

Required Qualifications:

Experience: At least 4 years of professional experience in machine learning, data science, or AI engineering, with a focus on deploying and maintaining production-level models.

Education: A Master's or Ph.D. in Computer Science, Data Science, Machine Learning, Statistics, or a related field.

Technical Skills:

1. Programming Languages: Strong proficiency in Python, with experience in machine learning libraries such as Scikit-learn, TensorFlow, Keras, PyTorch, XGBoost, etc.
2. Data Processing & Pipelines: Experience with data preprocessing techniques, feature engineering, and tools such as Pandas, NumPy, Dask, or Spark.
3. Machine Learning Algorithms: Expertise in a wide range of machine learning algorithms, including supervised learning, unsupervised learning, reinforcement learning, and deep learning (e.g., CNN, RNN, LSTM, GANs).
4. Model Deployment: Experience deploying models into production using Docker, Kubernetes, and cloud platforms like AWS, Google Cloud, or Azure.
5. Model Performance Metrics: In-depth understanding of model evaluation metrics, such as ROC/AUC, F1-score, Precision/Recall, confusion matrix, and cross-validation.
6. NLP & Computer Vision: Familiarity with NLP (e.g., transformers, BERT, GPT) and computer vision (e.g., OpenCV, YOLO, image classification).
7. Big Data Tools: Experience working with big data frameworks like Hadoop, Spark, and distributed computing systems.
8. Mathematical & Statistical Knowledge: Strong foundation in probability, statistics, linear algebra, and calculus, applied in machine learning modeling.

Desired Skills & Attributes:

1. Leadership: Ability to take the lead on complex machine learning projects and mentor junior team members.
2. Problem-Solving: Ability to approach complex business challenges and deliver innovative, data-driven solutions.
3. Communication: Strong ability to present complex machine learning concepts to both technical and non-technical stakeholders.
4. Collaboration: Strong team player who can work closely with data scientists, software engineers, and business teams.
5. Adaptability: Ability to adapt to new tools, algorithms, and technologies quickly.
6. Attention to Detail: Keen eye for identifying errors and ensuring the accuracy and reliability of machine learning models.

Benefits:

1. Competitive salary based on experience.
2. Health insurance and other company benefits.
3. Opportunities for professional development, certifications, and attending AI/ML conferences.
4. Flexible working hours and a collaborative work environment.
5. The chance to work on impactful, cutting-edge machine learning projects.