Ai Architect

**Responsibilities**

**Strategic Roles and Responsibilities**
- Build a robust enterprise-wide architecture for AI with the Applied AI Team.
- Evaluate feedback and challenging issues gathered by the business and technical teams and govern and scale the AI initiatives accordingly.
- Envision, build, deploy and operationalize an end-to-end machine learning (ML) and AI pipeline in collaboration with the Data, ML and Solutions Team.
- Participate in research paper publishing, Applied AI Competitions; organize hackathons and knowledge conferences/webinars.
- Ensure technical framework adaptability and architect AI Solutions that fit with the IT ecosystem of the Adani business units ensuring full scale implementation.

**Operational Responsibilities**
- Discuss the feasibility of use cases along with architectural design with business teams (AI Champions, Business Data Analysts) and translate the vision of business leaders into realistic technical implementation, along with the Solution Manager.
- Align technical implementation with existing and future requirements by gathering inputs from multiple stakeholders and develop 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.
- 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.
- 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.
- Design blueprints on how to structure, store and utilize data.
- Analyse existing data management processes and points out inefficiencies, and potential risks.
- Develop a solution schematic based on a broad design concept, and business needs.

**Stakeholder Management**
- Collaborate with AI Champion and Business Data Analyst to understand business and data requirements, nature and sources.
- Collaborate with AI Solution Managers, Data Engineers and ML Engineers to build overall and solution-specific AI Architecture and to ensure smooth and structured delivery.
- Engage with the Business IT teams to ensure solutions are compatible with the overall IT ecosystem.

**Experience, Educational Qualification and Skills**

**Experience**:

- 5-8 years of relevant work experience

**Educational Qualification**:

- B.E/B.Tech and M.E./M.Tech (Computer Science)

**Skills**:
**Mandatory**
- Should have expertise in creating solution design documentation
- Knowledge of any of the cloud solutions such as Azure or AWS
- Knowledge of ML and Deep Learning frameworks such as Keras, Tensor Flow
- Solid understanding and experience of deep learning architectures and algorithms
- In-depth knowledge of architectural components involved across the data management, governance, model building, deployment and production workflows of AI
- Software engineering and DevOps principles, including knowledge of DevOps workflows and tools, such as Git, containers, Kubernetes and CI/CD.
- Knowledge of advanced analytics tools (such as SAS, R and Python) along with applied mathematics
- Knowledge of ML techniques such as random forest and neural networks

**Preferred**
- Skills in Java/C are a plus

**Behavioural**
- Capacity for effective communication and technical documentation skills
- Strong Program Management and thought leadership skills
- Collaborative skills to work with a multi culture global teams
- Ability to build strong relationship with project stakeholders

**People working in below domains (Preferred but not restricted to)**:
Energy Generation (Thermal, Renewable) Transmission Distribution, City Gas Distribution Sectors, Airport, Port & Logistics, Cyber Security, E-Commerce, FMCG, Natural Resource, Healthcare, Infrastructure, Défense