Role Overview :

As a Lead AI/ML Architect, you will serve as the technical anchor for our most complex machine learning initiatives. You will bridge the gap between high-level business requirements and robust technical execution, designing end-to-end architectures that transition from experimental models to high-availability production environments. Working closely with cross-functional engineering teams, data scientists, and key stakeholders, you will define the standards for model deployment, scalability, and performance monitoring. Your work will directly dictate the efficiency and reliability of the AI products that drive our clients' strategic decision-making processes.

Key Responsibilities :

- Architect and oversee the development of scalable AI/ML pipelines to ensure seamless integration between data ingestion, model training, and real-time inference.

- Define and implement MLOps best practices to automate model lifecycle management, reducing deployment cycles and improving system reliability for enterprise clients.

- Lead the design of cloud-native AI infrastructure, ensuring optimal resource utilization and cost-efficiency across multi-cloud or hybrid environments.

- Mentor engineering teams on advanced deep learning techniques and architectural patterns to elevate the collective technical capability of the organization.

- Collaborate with business stakeholders to translate complex operational challenges into technical roadmaps, ensuring that AI initiatives deliver measurable business value.

Required Skillset :

- Demonstrated expertise in designing and deploying large-scale AI architectures, with a deep understanding of the end-to-end machine learning lifecycle.

- Proficiency in architecting cloud-based solutions on platforms like AWS, Azure, or GCP, with a focus on high-availability and distributed computing.

- Strong command over deep learning frameworks and machine learning libraries, coupled with the ability to select the right tools for specific business problems.

- Proven experience in implementing MLOps frameworks to manage model versioning, continuous integration, and continuous deployment in production environments.

- Exceptional communication skills, with the ability to articulate complex technical strategies to non-technical stakeholders and influence decision-making at the leadership level.

- A minimum of 6 - 12 years of professional experience in machine learning and software architecture, preferably within a high-growth consulting or product environment.

- A degree in Computer Science, Engineering, or a related quantitative field, reflecting a strong foundation in algorithmic thinking and system design.

- Ability to work effectively in a hybrid setup in Mumbai, maintaining high levels of collaboration and productivity across distributed teams.