AI Architect

10 hours ago

India Ally-eXecutive HR Full time

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.

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