AI Architect

We are looking for Architects with exp in AI, ML / DL. Min 2 years in architecture to 6 years is required.

Key Responsibilities :

Worked on the AI / ML architecture roadmap aligned with organizational strategy, ensuring scalability, reliability, and security.

Architect and develop AI solutions : data pipelines, model development, deployment, monitoring, and governance.

Develop and Lead the implementation of MLOps frameworks for CI / CD, model registry, reproducibility, drift detection, and lifecycle management.

Evaluate and select suitable AI / ML frameworks, tools, and cloud platforms; ensure optimal use of technologies.

Partner with data engineering, product, and business teams to identify opportunities for AI adoption and design scalable solutions.

Provide technical leadership and mentorship to ML engineers, data scientists, and developers.

Ensure compliance with data security, ethical AI, and regulatory standards in AI system design.

Drive innovation by staying updated on emerging AI research, trends, and best practices.

Required Skills & Qualifications

6–10 years of relevant experience in AI / ML engineering, data science, or AI solution architecture.

Strong proficiency in Python (preferred) and familiarity with enterprise-grade programming practices.

Deep expertise in ML / DL frameworks : TensorFlow, PyTorch, scikit-learn.

Proven experience in architecting and deploying AI solutions on cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML).

Solid understanding of data engineering, distributed systems, and API / microservices-based architectures.

Knowledge of big data tools (Spark, Kafka, Hadoop) and data pipeline orchestration.

Strong grounding in statistics, probability, linear algebra, optimization, and algorithm design.

Experience designing systems with observability, fault tolerance, scalability, and cost-efficiency.

Bachelor’s or Master’s degree in Computer Science, Engineering or related field.

Preferred Qualifications :

Experience with enterprise AI governance frameworks, including model explainability and responsible AI.

Hands-on expertise with containerization (Docker, Kubernetes) and distributed model training.

Contributions to open-source AI projects, patents, or publications in applied AI / ML.

Familiarity with GenAI (LLMs, transformers, prompt engineering) and applied use cases in enterprises.

Soft Skills & Qualities

∙Strategic thinker with the ability to connect business goals with AI capabilities.

∙Strong development and mentoring skills; able to guide cross-functional teams.