3 Virtusa Consulting Services Data Scientist Jobs

AI Engineer:
Design and implement generative AI models, including GANs, VAEs, and transformers, as well as large language models such as GPT, BERT, LLAMA and similar to create high quality synthetic data and other outputs.
Use cutting edge technologies to create a faster, more capable AI.
Preprocess and analyse large datasets to train and validate AI and LLM models.
Analyse statistics, data, and algorithms for projection accuracy.
Use AI analysis to make strategy recommendations that align with company goals.
Work with a team of machine learning and data engineers to ensure seamless AI development and integration.
Collaborate with software engineers to integrate AI models into production systems, ensuring scalability, reliability, and performance.
Stay current on AI knowledge, trends, regulations and proactively apply them to enhance existing solutions.
Collect, organize, and present progress with team leadership and stakeholders.
Maintain thorough documentation of models, experiments, and methodologies.
Qualifications:
Proven experience in developing and deploying machine learning models in real world applications.
Proficiency in programming languages such as Python, Java, or C++, with experience in libraries and frameworks for AI development.
Strong understanding of machine learning algorithms, deep learning architectures, and optimization techniques.
Hands-on experience with GANs, VAEs, transformers and LLMs such as GPT, BERT, LLAMA and similar with experience on TensorFlow, PyTorch, Keras, or Apache MXNet.
Familiarity with natural language processing libraries such as NLTK, spaCy, or Transformers.
Experience in computer vision tasks using frameworks like OpenCV, TensorFlow Object Detection API, or PyTorch Vision.
Familiarity with cloud platforms like AWS, GCP, or Azure.
Excellent problem-solving skills and the ability to work effectively in a fast-paced, collaborative environment.
Effective communication skills with the ability to convey complex technical concepts to non-technical stakeholders.
Demonstrated ability to manage multiple projects simultaneously and deliver high-quality results on time.