Knowledge of machine learning algorithms and concepts (e.g., supervised learning, unsupervised learning, deep learning) as applied to generative AI.
5+ years of professional experience in a technical role developing, training, evaluating and deploying ML solutions at scale for real-world problems
5+ years of experience as a software engineer, developing and shipping software in Python, C#, Java or modern language equivalent.
Familiarity with ML frameworks and libraries like TensorFlow, PyTorch, Scikit-learn, Keras, etc.
Experience in handling large datasets and working with data processing frameworks (Apache Spark, Hadoop etc.)
Hands-on experience with cloud platforms like Azure, AWS or GCP for deploying and scaling machine learning models.
Excellent cross-group and interpersonal skills, with the ability to articulate solutions.
Bachelors/Master’s degree with relevant course work toward Computer Science, Data Science, Statistics, Machine Learning, Data Mining and equivalent work experience.
Preferred Qualifications:
Experience in designing and implementing MLOps strategies for model deployment, monitoring, and governance.
Familiarity in deep learning architectures (Transformers, CNNs, RNNs) and knowledge of Natural Language Processing (NLP)
Knowledge of containers (Docker) and orchestration tools like Kubernetes.
Strong analytical mind and a confident decision maker
Excellent computer science fundamentals in algorithmic design, data structures, and analyzing complexity
Ability to balance competing demands and adapt to changing priorities
Experience mentoring junior engineers and data scientists, providing technical guidance and code reviews.
Responsibilities
Responsibilities
Design, develop, and implement robust and scalable software applications that utilize generative AI techniques.
Integrate LLMs and NLP into software solutions for tasks such as content recommendations, summarization, translation, and retrieval.
Optimize generative AI models and their performance for specific use cases.
Help establish and drive the adoption of good coding standards and patterns.
Help identify opportunities to improve and optimize existing systems using generative AI.
Stay up-to-date with the latest trends and technologies in generative AI.