Work with advanced GenAI models and LLMs (e.g., ChatGPT, FlanT-5, Llama, Cohere) to develop innovative solutions for chatbot systems, content summarization, and other AI-driven applications.
Fine-tune and optimize pre-trained models for specific business use cases.
Implement Reinforcement Learning with Human Feedback (RLHF) to improve model performance and user interaction quality.
Model Development & Fine-Tuning:
Design and develop vision models and multi-agent systems.
Conduct prompt engineering to improve language models' accuracy and response relevance.
Apply techniques like retrieval-augmented generation (RAG) to enhance model responses with external data.
AI Systems & Applications:
Design and deploy scalable AI applications using platforms such as Langchain, Hugging Face, and Streamlit.
Develop and integrate chatbots, summarization tools, and other NLP-based applications.
Apply FMEA (Failure Mode and Effect Analysis) to model design to ensure robustness and reliability.
Machine Learning & Deep Learning:
Develop and deploy machine learning models for various tasks, including regression, classification, and neural networks.
Build and optimize deep learning models to solve complex problems across industries.
Cloud AI Deployment:
Deploy machine learning models at scale using AWS SageMaker and AWS Bedrock for efficient model training and inference.
Collaborate with cloud engineers to ensure seamless integration with cloud infrastructure and services.
Collaboration & Documentation:
Work closely with cross-functional teams, including data scientists, engineers, product managers, and business stakeholders.
Maintain thorough documentation of AI models, training procedures, model versions, and deployment processes.
Mentor junior engineers and data scientists, sharing expertise in AI/ML technologies and best practices.
Familiarity with crewAI, Comet for AI model tracking and collaboration.
Experience in working with MongoDB or other NoSQL databases to store model-related data.
Familiarity with the latest advancements in ontology and its application in AI systems.
Understanding of multi-agent systems and how they can be used for distributed problem-solving.
Knowledge of AWS Lambda and API Gateway for serverless deployment of AI models.
Generative AI & LLMs:
Experience with large language models like ChatGPT, FlanT-5, Llama, and Cohere.
Expertise in prompt engineering and fine-tuning LLMs for various applications (e.g., chatbots, summarization).
Hands-on experience with Reinforcement Learning with Human Feedback (RLHF) techniques.
Machine Learning & Deep Learning:
Strong foundation in machine learning techniques such as regression and classification.
Experience working with neural networks and deep learning models (CNNs, RNNs, Transformers).
Familiarity with common machine learning frameworks (e.g., TensorFlow, PyTorch).
AI Frameworks & Libraries:
Experience with Langchain, Hugging Face, and other frameworks for building and deploying AI models.
Familiarity with Streamlit for building interactive AI applications.
Experience in implementing RAG (retrieval-augmented generation) for more informative and accurate responses from models.
Cloud Platforms o Expertise in using AWS SageMaker for model training, hyperparameter tuning, and deployment.
Generative AI & Large Language Models:
Work with advanced GenAI models and LLMs (e.g., ChatGPT, FlanT-5, Llama, Cohere) to develop innovative solutions for chatbot systems, content summarization, and other AI-driven applications.
Fine-tune and optimize pre-trained models for specific business use cases.
Implement Reinforcement Learning with Human Feedback (RLHF) to improve model performance and user interaction quality.
Model Development & Fine-Tuning:
Design and develop vision models and multi-agent systems.
Conduct prompt engineering to improve language models' accuracy and response relevance.
Apply techniques like retrieval-augmented generation (RAG) to enhance model responses with external data.
AI Systems & Applications:
Design and deploy scalable AI applications using platforms such as Langchain, Hugging Face, and Streamlit.
Develop and integrate chatbots, summarization tools, and other NLP-based applications.
Apply FMEA (Failure Mode and Effect Analysis) to model design to ensure robustness and reliability.
Machine Learning & Deep Learning:
Develop and deploy machine learning models for various tasks, including regression, classification, and neural networks.
Build and optimize deep learning models to solve complex problems across industries.
Cloud AI Deployment:
Deploy machine learning models at scale using AWS SageMaker and AWS Bedrock for efficient model training and inference.
Collaborate with cloud engineers to ensure seamless integration with cloud infrastructure and services.
Collaboration & Documentation:
Work closely with cross-functional teams, including data scientists, engineers, product managers, and business stakeholders.
Maintain thorough documentation of AI models, training procedures, model versions, and deployment processes.
Mentor junior engineers and data scientists, sharing expertise in AI/ML technologies and best practices.
Familiarity with crewAI, Comet for AI model tracking and collaboration.
Experience in working with MongoDB or other NoSQL databases to store model-related data.
Familiarity with the latest advancements in ontology and its application in AI systems.
Understanding of multi-agent systems and how they can be used for distributed problem-solving.
Knowledge of AWS Lambda and API Gateway for serverless deployment of AI models.
Generative AI & LLMs:
Experience with large language models like ChatGPT, FlanT-5, Llama, and Cohere.
Expertise in prompt engineering and fine-tuning LLMs for various applications (e.g., chatbots, summarization).
Hands-on experience with Reinforcement Learning with Human Feedback (RLHF) techniques.
Machine Learning & Deep Learning:
Strong foundation in machine learning techniques such as regression and classification.
Experience working with neural networks and deep learning models (CNNs, RNNs, Transformers).
Familiarity with common machine learning frameworks (e.g., TensorFlow, PyTorch).
AI Frameworks & Libraries:
Experience with Langchain, Hugging Face, and other frameworks for building and deploying AI models.
Familiarity with Streamlit for building interactive AI applications.
Experience in implementing RAG (retrieval-augmented generation) for more informative and accurate responses from models.
Cloud Platforms o Expertise in using AWS SageMaker for model training, hyperparameter tuning, and deployment.
Key job details
About Virtusa
Teamwork, quality of life, professional and personal development: values that Virtusa is proud to embody. When you join us, you join a team of 36,000 people globally that cares about your growth — one that seeks to provide you with exciting projects, opportunities and work with state of the art technologies throughout your career with us.
Great minds, great potential: it all comes together at Virtusa. We value collaboration and the team environment of our company, and seek to provide great minds with a dynamic place to nurture new ideas and foster excellence.
Virtusa is an Equal Opportunity Employer. All applicants will receive fair and impartial treatment without regard to race, color, religion, sex, national origin, ancestry, age, legally protected physical or mental disability, protected veteran status, status in the U.S. uniformed services, sexual orientation, gender identity or expression, marital status, genetic information or on any other basis which is protected under applicable federal, state or local law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government-issued ID during each interview. All candidates must be authorized to work in the USA.
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