Design, develop, and deploy machine learning and deep learning models for a variety of business use cases
Design and develop NLP and generative AI models using architectures like transformers, GPT, BERT, etc.
Fine-tune, prompt-engineer, or distill pre-trained LLMs for domain-specific tasks (e.g., summarization, Q&A, classification)
Collaborate with cross-functional teams to identify business opportunities and translate them into data-driven solutions
Implement and maintain data pipelines for model training, evaluation, and deployment
Integrate external and internal data sources, including unstructured data, into knowledge graphs and vector databases like (ChromaDB, FAISS, Neo4j)
Evaluate and select appropriate frameworks and tools for AI/ML projects (e.g., TensorFlow, PyTorch, Hugging Face, LangChain)
Monitor model performance and retrain models as necessary to ensure accuracy and relevance
Document processes, models, and code to ensure reproducibility and knowledge sharing

Bachelor degree in Engineering, Engineering Technology, Computer Science, Data Science, Mathematics, Physics, Chemistry, or non-US equivalent qualifications directly related to the work statement
Proficiency in Python and basic understanding of data science libraries (e.g., NumPy, pandas, scikit-learn)
Familiarity with deep learning frameworks such as TensorFlow or PyTorch
Basic experience with at least one generative AI framework (e.g., Hugging Face Transformers, LangChain, LangGraph)
Strong understanding of machine learning algorithms, model evaluation, and deployment best practices
Knowledge of cloud platforms (OpenShift , Kubernetes , Docker) for model training and deployment
Excellent problem-solving, communication, and collaboration skills

Experienced Test & Evaluation Engineer

Key skills