Artificial Intelligence Specialist

Job Description – AI Architect (6–10 years)

Role Summary

6–10 years of experience

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.