Project Description:
As an AI Engineer, you will be responsible for designing, developing, deploying and maintaining AI models and systems. You will work closely with cross-functional teams to understand business requirements and translate them into scalable and reliable AI systems. The ideal candidate should have a strong background in machine learning, software development, and a passion for innovation.
Job Description:
• AI Model Development:
o Develop, train, and validate machine learning models.
o Experiment with different algorithms and architectures to solve complex problems.
o Fine-tune models for optimal performance.
o Apply techniques such as regularization, cross-validation, and hyperparameter tuning.
• Data Preparation and Pre-processing:
o Collect, clean, and preprocess raw data.
o Conduct exploratory data analysis.
o Transform data into a format suitable for training AI models.
• Model Deployment:
o Deploy trained models into production environments.
o Integrate models into existing systems.
o Implement APIs for model inference.
o Monitor and maintain deployed models.
• Performance Monitoring and Optimization:
o Monitor the performance of deployed models.
o Identify and fix issues related to model performance.
o Optimize models for speed, accuracy, and resource utilization.
o Implement A/B testing to evaluate model changes.
• Collaboration:
o Work closely with cross-functional teams, including software engineers, data scientists, and domain experts.
o Communicate effectively with non-technical stakeholders.
o Participate in code reviews and design discussions.
Responsibilities:
• Develop and optimize Langchain-heavy applications for various LLM-driven workflows
• Design, build and maintain API endpoints written in Flask to expose LLM models.
• Integrate LLMs (Huggingface, OpenAI, etc.) into scalable applications.
• Work with vector databases (ChromaDB, Redis) for RAG proccess.
• Implement prompt engineering strategies to improve model performance.
• Manage SQL tables (mysql)
• Deploy and manage models/projects on AWS (EC2) and/or Openshift using Docker containers
Mandatory Skills:
• 6+ years of relevant working experience
• Bachelor's/Master's/Ph.D. in Computer Science, Engineering, Mathematics, or related field.
• Proven experience in developing and deploying machine learning models.
• Proficiency in programming languages such as Python, and frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Strong understanding of data structures, algorithms, and statistical techniques.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration skills.
Preferred Qualifications:
• Experience with containerization technologies such as Docker and orchestration tools like Kubernetes.
• Knowledge of big data technologies such as Hadoop, Spark, or Kafka.
• Experience with data engineering, creating data pipelines etc.
• Familiarity with software development best practices, including version control, testing, and continuous integration/continuous deployment (CI/CD).
• Publications in peer-reviewed journals or conferences related to artificial intelligence and machine learning.
Languages:
- English: C1 Advanced
Project Description:
As an AI Engineer, you will be responsible for designing, developing, deploying and maintaining AI models and systems. You will work closely with cross-functional teams to understand business requirements and translate them into scalable and reliable AI systems. The ideal candidate should have a strong background in machine learning, software development, and a passion for innovation.
Job Description:
• AI Model Development:
o Develop, train, and validate machine learning models.
o Experiment with different algorithms and architectures to solve complex problems.
o Fine-tune models for optimal performance.
o Apply techniques such as regularization, cross-validation, and hyperparameter tuning.
• Data Preparation and Pre-processing:
o Collect, clean, and preprocess raw data.
o Conduct exploratory data analysis.
o Transform data into a format suitable for training AI models.
• Model Deployment:
o Deploy trained models into production environments.
o Integrate models into existing systems.
o Implement APIs for model inference.
o Monitor and maintain deployed models.
• Performance Monitoring and Optimization:
o Monitor the performance of deployed models.
o Identify and fix issues related to model performance.
o Optimize models for speed, accuracy, and resource utilization.
o Implement A/B testing to evaluate model changes.
• Collaboration:
o Work closely with cross-functional teams, including software engineers, data scientists, and domain experts.
o Communicate effectively with non-technical stakeholders.
o Participate in code reviews and design discussions.
Responsibilities:
• Develop and optimize Langchain-heavy applications for various LLM-driven workflows
• Design, build and maintain API endpoints written in Flask to expose LLM models.
• Integrate LLMs (Huggingface, OpenAI, etc.) into scalable applications.
• Work with vector databases (ChromaDB, Redis) for RAG proccess.
• Implement prompt engineering strategies to improve model performance.
• Manage SQL tables (mysql)
• Deploy and manage models/projects on AWS (EC2) and/or Openshift using Docker containers
Mandatory Skills:
• 6+ years of relevant working experience
• Bachelor's/Master's/Ph.D. in Computer Science, Engineering, Mathematics, or related field.
• Proven experience in developing and deploying machine learning models.
• Proficiency in programming languages such as Python, and frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Strong understanding of data structures, algorithms, and statistical techniques.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration skills.
Preferred Qualifications:
• Experience with containerization technologies such as Docker and orchestration tools like Kubernetes.
• Knowledge of big data technologies such as Hadoop, Spark, or Kafka.
• Experience with data engineering, creating data pipelines etc.
• Familiarity with software development best practices, including version control, testing, and continuous integration/continuous deployment (CI/CD).
• Publications in peer-reviewed journals or conferences related to artificial intelligence and machine learning.
Languages:
- English: C1 Advanced