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As a digital transformation and cloud solutions company with strong expertise in Financial Services, Insurance Services, Retail Solutions and Learning solutions, we innovate and create a niche for organizations in an ever-evolving digital landscape
We leverage the power of design, cutting-edge engineering, and cloud technologies to build web and mobile apps, enabling digital transformation for businesses. Our expertise in handling the technological backbone of large enterprises coupled with strong domain knowledge, helps us to bring differentiation into our products and solutions.
Job ProfileWe are looking for curators and owners of the AI architecture strategy. They will be a link between data scientists, data engineers, developers, operations and business unit leaders to govern and scale the AI initiatives. We expect them to work closely with enterprise and solution architects, focusing on building a robust enterprise wide architecture for AI. Previous experience of BFSI domain used case will be an added advantage.
Education QualificationBachelor’s / Master's Degree in Machine Learning, Statistics, Computer Science, Physics, Math, or related field and with an experience of 4-10 years in technology architecture, designing, developing and implementing customized IT solutions on AI / ML systems.
Job DescriptionCollaborate with data scientists and other AI professionals to augment digital transformation efforts.Align technical implementation with existing and future requirements.Play a key role in defining the AI architecture and selecting appropriate technologies.Audit AI tools and practices across data, models and software engineering.Work closely with security and risk leaders to foresee and overturn risks.3-5 relevant years’ experience on NLP, Machine Learning and Deep learningGood to have knowledge of Generative AI, to know (one or more) Machine learning, Statistics, Time Series, Deep learning.Build and lead AI/ML team within product engineering and work with cross-functional teams in identifying and prioritizing key areas of AI/ML development.
Technical skillsAI architecture and pipeline planning . Understand the workflow and pipeline architectures of AIML workloads. An in-depth knowledge of components and architectural trade-offs involved across the data management, governance, model building, deployment and production workflows of AI is a must.Software engineering and DevOps principles , including knowledge of DevOps workflows and tools, such as Git, containers, Kubernetes and CI/CD.Data science and advanced analytics , including knowledge ofAdvanced analytics tools such as SAS, R and Python) along with applied mathematics.ML and Deep Learning frameworks such as TensorFlow, PyTorch, Keras or similarML algorithms such as like Random Forest, CNN, RCNN, etc.Libraries such as like Scikit learn, Scipy, Theano, Matplotlib / Seaborn, Caffe, etc.
Non-technical skillsThought leadership . Be change agents to help the organization adopt an AI-driven mind set. Take a pragmatic approach to the limitations and risks of AI, and project a realistic picture in front of IT executives who provide overall digital thought leadership.Collaborative mind set . To ensure that AI platforms deliver both business and technical requirements, seek to collaborate effectively with data scientists, data engineers, data analysts, ML engineers, other architects, business unit leaders (technical and nontechnical personnel), and harmonize the relationships among them.