Senior AI/ML Architect

17 hours ago


Multiple Locations, India Zorba Consulting Full time ₹ 20,00,000 - ₹ 25,00,000 per year

Job Summary :

We are seeking a senior AI/ML Architect to lead the design and implementation of our AI and machine learning initiatives.

You will be responsible for creating the architectural vision and blueprints for scalable, secure, and performant ML systems.

This is a strategic role that requires a blend of deep technical knowledge, leadership, and excellent communication skills to translate business needs into robust technical solutions.


Key Responsibilities :

- Define the overall technical architecture for AI/ML projects, including model development, training, deployment, and serving infrastructure.

- Evaluate and select appropriate ML frameworks, cloud platforms, and tools to meet project requirements and ensure a future-proof architecture.

- Collaborate with data scientists, ML engineers, and business stakeholders to align the technical roadmap with strategic goals.

- Design and implement scalable data pipelines and feature stores to support ML model training and inference.

- Establish best practices for model versioning, reproducibility, and governance.

- Provide technical leadership and mentorship to the engineering teams.

- Stay abreast of cutting-edge research and industry trends in AI/ML and recommend new technologies to enhance our platform.


Required Qualifications :

- 5+ years of experience in a software or machine learning engineering role.

- 3+ years of experience in an architecture-focused role, with a strong emphasis on AI/ML systems.

- Expertise in a relevant programming language (e.g., Python, Java, Go).

- Deep knowledge of ML frameworks (TensorFlow, PyTorch, or similar).

- Extensive experience with cloud computing platforms (AWS, GCP, or Azure) and their AI/ML services.

- Excellent leadership, communication, and stakeholder management skills.


Preferred Qualifications :

- A Master's or PhD in Computer Science, AI, or a related technical field.

- Experience with distributed training and inference.

- Direct experience with MLOps and infrastructure as code (IaC).

- Contributions to open-source projects in the AI/ML ecosystem



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