AI Architect - Noida, India - EXL

Description

Proven expertise to drive AI leadership capabilities to drive new revenues, efficiencies and customer experience for larger enterprises.

As part of your duties, you will be responsible for:

Experience with having deployed AI solutions and systems at large enterprises

Proven expertise in working with clients to design and build AI applications and systems that drive productivity and differentiation for enterprises – AI consulting experience preferred

Experience working in at least one of Insurance, Banking, Healthcare or transportation and logistics

Experience in leading AI R&D teams including new product development for AI-powered solutions

Exhibited thought leadership in the AI space

Built and mentored AI R&D and AI deployment teams.

Build robust enterprise wide architecture for AI solutions for clients - envision, build, deploy and operationalize an end-to-end machine learning and AI pipeline.

Work closely with enterprise and solution architects, to translate business and solution requirements to enterprise-wide AI architecture

Collaborate with data scientists and other AI professionals to augment digital transformation efforts by identifying and piloting use cases. Discuss the feasibility of use cases along with architectural design with business teams and translate the vision of business leaders into realistic technical implementation. At the same time, bring attention to misaligned initiatives and impractical use cases.

Align technical implementation with existing and future requirements by gathering inputs from multiple stakeholders — business users, data scientists, security professionals, data engineers and analysts, and those in IT operations — and developing processes and products based on the inputs.

Play a key role in defining the AI architecture and selecting appropriate technologies from a pool of open-source and commercial offerings. Select cloud, on-premises or hybrid deployment models, and ensure new tools are well-integrated with existing data management and analytics tools.

Practitioners' ability to identify and infuse generative AI capabilities across all AI solutions that we conceptualize, build, deploy and take to market

Develop and implement state-of-the-art generative AI models and algorithms.

Conduct research and experimentation to improve existing models and propose novel approaches.

Collaborate with cross-functional teams to integrate generative AI solutions into real-world applications.

Stay up-to-date with the latest advancements in deep learning and generative models and apply them to enhance our AI capabilities.

Document research findings, prepare technical reports, and contribute to whitepaper/scientific publications.

Provide deep leadership and coaching in the project delivery lifecycle. Focus on shared learning, continuous improvement, and drive adoption of best practices.

Oversee the design and development of AI products and infrastructure for use across industries in B2C and B2B contexts

Requirement Analysis: Collaborate with stakeholders to understand business needs and identify opportunities where AI can provide value and address challenges.

Solution Design: Develop architectural designs and system blueprints that outline the components, algorithms, and technologies required to build AI solutions.

Algorithm Selection: Assess various AI algorithms, models, and frameworks to determine the most suitable ones for the specific use case and problem.

Data Processing: Design data pipelines and strategies for acquiring, preprocessing, cleaning, and transforming data to ensure its suitability for AI model training and deployment.

Model Development: Oversee the development and training of AI models, selecting appropriate techniques such as machine learning, deep learning, or reinforcement learning.

Performance Optimization: Fine-tuning AI models and algorithms to improve accuracy, efficiency, and scalability while considering speed, memory usage, and computational resources.

Ethical Considerations: Ensure ethical and responsible AI practices by addressing bias, fairness, privacy, and transparency throughout the AI system's lifecycle.

Integration and Deployment: Collaborate with development teams to integrate AI systems into existing infrastructure, ensuring seamless deployment, scalability, and interoperability.

Testing and Validation: Conduct thorough testing and validation to assess AI systems' performance, reliability, and robustness and iteratively refine them based on feedback and evaluation.

Audit AI tools and practices across data, models and software engineering with a focus on continuous improvement. Ensure a feedback mechanism to assess AI services, support model recalibration and retrain models.

Work closely with security and risk leaders to foresee and overturn risks, such as training data poisoning, AI model theft and adversarial samples, ensuring ethical AI implementation and restoring trust in AI systems. Remain acquainted with upcoming regulations and map them to best practices.

Skills

Ability to define the AI architecture strategy and technical specs for enterprise transformation

Ability to orchestrate execution of the technical strategy across data scientists, data engineers, developers, operations (DevOps, DataOps, MLOps) and business unit leaders to govern and scale the AI initiatives

AI architecture and pipeline planning - Understand the workflow and pipeline architectures of ML and deep learning 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 of advanced analytics tools (such as SAS, R and Python) along with applied mathematics, ML and Deep Learning frameworks (such as TensorFlow) and ML techniques (such as random forest and neural networks).

Auditing AI tools, ensuring model interoperability, foreseeing and designing to address risks

Strong educational background with a completed (or on-track to complete) PhD in deep learning, machine learning, related area, or equivalent experience.

Solid knowledge of deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models.

Familiarity with key concepts and techniques used in generative models, such as variational autoencoders (VAEs), generative adversarial networks (GANs), and flow-based models.

Strong programming skills in languages such as Python, along with experience working with popular deep learning frameworks like PyTorch and TensorFlow.

Experience with large-scale data processing and distributed computing frameworks (e.g., Apache Spark, TensorFlow distributed).

Understanding of Graph Database and Vector Database along with knowledge of cloud computing platforms (e.g., AWS, Google Cloud).

Experience with deploying AI models in production environments.

Proficiency in additional programming languages, such as C++, Java, or R.

Familiarity with domain-specific applications of generative AI, such as computer vision, natural language processing, or healthcare.

Experiences

Track record in relevant research, such as deep learning and deep generative models, as evidenced by publications in top-tier venues (e.g., ICML, NeurIPS, ICLR, UAI, AIStats).

Hands-on experience with current deep learning frameworks (e.g., PyTorch, TensorFlow) as evidenced by released code (e.g., GitHub repositories – version control awareness).

Solid understanding of optimization techniques for training deep neural networks, regularization methods, and hyperparameter/fine tuning.

Experience in Generative AI Models (Text to Text, Text to Image), finetuning, prompt engineering and experience with frameworks like Langchain

Strong software engineering skills for rapid and accurate development of AI models and systems.

Provide business-oriented solution with ability to communicate effectively, both verbally and in writing, with technical and non-technical stakeholders.

Experience working in a collaborative environment, contributing to multidisciplinary teams and projects.

Proven ability to solve complex problems, think creatively, and adapt to evolving research trends.