AI Architect
1 day ago
We are looking for a seasoned AI Architect with 6–10 years of experience to define, develop and implementation of AI/ML solutions at enterprise scale. This role requires deep expertise in AI/ML technologies, architecture design, and MLOps, combined with strong skills to develop and deploy and align AI initiatives with business goals.
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.
- 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.
- 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.
- Strategic thinker with the ability to connect business goals with AI capabilities.
- Strong development and mentoring skills; able to guide cross-functional teams.
- Excellent communication and storytelling skills to engage both technical and non-technical stakeholders.
- Growth mindset and passion for driving innovation in AI.
- Ethical and responsible in the application of AI technologies.
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