Data Scientist / ML Engineer

Overview: We are seeking a talented and innovative Data Scientist to join our growing team. In this role, you will be responsible for applying machine learning and generative AI techniques to solve complex problems, drive business insights, and enhance product offerings. You will collaborate with engineers to build and deploy ML models in microservices architectures, ensuring the solutions are scalable, maintainable, and integrated with APIs.

Key Responsibilities:

• Analyze large, structured, and unstructured datasets to extract meaningful insights and identify business opportunities.
• Develop, test, and implement machine learning and generative AI models to drive intelligent decision-making and automation.
• Work closely with engineering teams to integrate machine learning models into production systems using microservices architectures.
• Design and develop APIs to enable seamless communication between data models and applications.
• Build and maintain scalable data pipelines to facilitate data collection, transformation, and storage for model training and inference.
• Utilize Python and relevant libraries (e. g. , Pandas, NumPy, TensorFlow, PyTorch) to preprocess data, train models, and perform statistical analysis.
• Collaborate with product and business teams to understand requirements and deploy machine learning solutions that meet business needs.
• Perform continuous monitoring, testing, and optimization of deployed models to ensure high performance and reliability.
• Stay updated on the latest trends and advancements in machine learning, generative AI , and related fields to apply cutting-edge techniques in your work.
• Document processes, methodologies, and model outputs for transparency and future improvements.

Required Skills & Qualifications:

• Bachelors or Masters degree in Computer Science, Data Science, Statistics, or related field.
• Proven experience in Python programming, including the use of libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, or Keras for machine learning.
• Hands-on experience with microservices architecture and containerization technologies like Docker or Kubernetes .
• Experience with building and deploying APIs to enable machine learning models to interact with other systems and applications.
• Strong understanding of Machine Learning algorithms, including supervised and unsupervised learning, deep learning, and generative AI techniques such as GANs (Generative Adversarial Networks) and language models.
• Ability to work with cloud platforms (AWS, GCP, or Azure) for model deployment and scalability.
• Knowledge of data engineering concepts, including data wrangling, ETL processes, and working with distributed systems.
• Familiarity with modern version control systems (e. g. , Git) and agile development practices.
• Strong analytical and problem-solving skills with the ability to communicate complex technical concepts to non-technical stakeholders.
• Experience with data visualization tools (e. g. , Tableau, Power BI, or Matplotlib) is a plus.

Preferred Qualifications:

• Experience with advanced Generative AI models, such as transformers (e. g. , GPT, BERT).
• Knowledge of DevOps practices and CI/CD pipelines for machine learning deployment.
• Familiarity with the integration of ML models into business applications and customer-facing products.
• Strong communication skills and the ability to collaborate with cross-functional teams including engineers, product managers, and business stakeholders.

Api, Python, Gen Ai, Machine Learning, Microservecies