Artificial Intelligence Architect - Machine Learning

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.