ID: 30109
We are looking for driven MLEs who enjoy solving problems, who initiate solutions and discussions and who believe that any challenge can be scaled with the right mindset and tools.
Key Job Responsibilities and Duties:
Deploying machine learning models: Design, develop and deploy in collaboration with scientists, scalable machine learning models and algorithms that provide content related insights and generative AI applications, ensuring scalability, efficiency, and accuracy.
Evaluating possible architecture solutions by taking into account cost, business requirements, emerging technologies, and technology requirements, like latency, throughput, and scale.
Generative AI Development: Contribute to the development of generative models such as GPT (Generative Pre-trained Transformer) variants or similar architectures for creative content generation, Q&A, translation or other innovative applications.
Deployment and integration: Work closely with software engineers to integrate ML models into production systems. Ensure flawless deployment and efficient model inference in real-time environments. Collaborate with DevOps to implement effective monitoring and maintenance strategies.
Leading a service end to end by actively monitoring application health and performance, setting and monitoring relevant metrics and acting accordingly when violated.
Maintain clean, scalable code, ensuring reproducibility and easy integration of models into production environments, including CI/CD.
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions.
We are looking for driven MLEs who enjoy solving problems, who initiate solutions and discussions and who believe that any challenge can be scaled with the right mindset and tools.
Key Job Responsibilities and Duties:
Deploying machine learning models: Design, develop and deploy in collaboration with scientists, scalable machine learning models and algorithms that provide content related insights and generative AI applications, ensuring scalability, efficiency, and accuracy.
Evaluating possible architecture solutions by taking into account cost, business requirements, emerging technologies, and technology requirements, like latency, throughput, and scale.
Generative AI Development: Contribute to the development of generative models such as GPT (Generative Pre-trained Transformer) variants or similar architectures for creative content generation, Q&A, translation or other innovative applications.
Deployment and integration: Work closely with software engineers to integrate ML models into production systems. Ensure flawless deployment and efficient model inference in real-time environments. Collaborate with DevOps to implement effective monitoring and maintenance strategies.
Leading a service end to end by actively monitoring application health and performance, setting and monitoring relevant metrics and acting accordingly when violated.
Maintain clean, scalable code, ensuring reproducibility and easy integration of models into production environments, including CI/CD.
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions.
Requirements:
Bachelors or masters degree in computer science, Engineering, Statistics, or a related field.
Minimum of 4 years of experience as a Machine Learning Engineer or a similar role, with a consistent record of successfully delivering ML solutions.
Strong programming skills in languages such as Python and Java.
Experience with cloud frameworks like AWS sagemaker for training, evaluation and serving models using TensorFlow, PyTorch, or scikit-learn.
Experience with big data processing frameworks such, Pyspark, Apache Flink, Snowflake or similar frameworks.
Experience with data at scale using MySQL, Pyspark, Snowflake and similar frameworks.
Proven experience with MySQL, Cassandra, DynamoDB or similar relational/NoSQL database systems.
Deep understanding of machine learning algorithms, statistical models, and data structures.
Experience in deploying large-scale language models like GPT, BERT, or similar architectures – an advantage.
Proficiency in data manipulation, analysis, and visualization using tools like NumPy, pandas, and matplotlib – an advantage.
Experience with experimental design, A/B testing, and evaluation metrics for ML models – an advantage.
Experience of working on products that impact a large customer base – an advantage.
Excellent communication in English; written and spoken.
Bachelors or masters degree in computer science, Engineering, Statistics, or a related field.
Minimum of 4 years of experience as a Machine Learning Engineer or a similar role, with a consistent record of successfully delivering ML solutions.
Strong programming skills in languages such as Python and Java.
Experience with cloud frameworks like AWS sagemaker for training, evaluation and serving models using TensorFlow, PyTorch, or scikit-learn.
Experience with big data processing frameworks such, Pyspark, Apache Flink, Snowflake or similar frameworks.
Experience with data at scale using MySQL, Pyspark, Snowflake and similar frameworks.
Proven experience with MySQL, Cassandra, DynamoDB or similar relational/NoSQL database systems.
Deep understanding of machine learning algorithms, statistical models, and data structures.
Experience in deploying large-scale language models like GPT, BERT, or similar architectures – an advantage.
Proficiency in data manipulation, analysis, and visualization using tools like NumPy, pandas, and matplotlib – an advantage.
Experience with experimental design, A/B testing, and evaluation metrics for ML models – an advantage.
Experience of working on products that impact a large customer base – an advantage.
Excellent communication in English; written and spoken.
This position is open to all candidates.
