AI Architect

AI Architect

Location: Remote

Job Description:

The AI Architect is a senior-level role responsible for designing, developing, and overseeing the implementation of advanced artificial intelligence (AI) and machine learning (ML) solutions. This position requires deep technical expertise, strategic vision, and leadership to align AI initiatives with business objectives. The ideal candidate will have 12-15 years of experience in AI/ML, software engineering, and system architecture, with a proven track record of delivering scalable AI solutions.

Key Responsibilities

• Architecture Design : Develop end-to-end AI/ML architectures, including data pipelines, model development, deployment, and integration into enterprise systems.

• Technical Leadership : Lead cross-functional teams of data scientists, engineers, and developers to deliver AI solutions, providing technical guidance and mentorship.

• Solution Development : Design and implement advanced AI models (e.g., deep learning, NLP, computer vision) tailored to business use cases.

Required Qualifications

• Experience : 12-15 years of professional experience in AI/ML, software engineering, or related fields, with at least 5 years in an AI architecture or leadership role.

• Education : Bachelor’s degree in computer science, Data Science, Engineering, or a related field. Master’s or Ph.D. in AI, Machine Learning, or a related discipline is preferred.

• Technical Skills :
  • Expertise in AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).

  • Open AI , Chat GPT and Prompt engineering

  • Proficiency in programming languages (e.g., Python, Java, C++).

  • Strong knowledge of cloud platforms (e.g., AWS, Azure, GCP) and their AI/ML services.

  • Experience with big data technologies (e.g., Hadoop, Spark, Kafka).

  • Familiarity with MLOps practices and tools (e.g., Kubeflow, MLflow, Docker, Kubernetes).

  • Understanding of database systems (e.g., SQL, NoSQL) and data engineering pipelines.

Preferred Qualifications

• Certifications in cloud platforms (e.g., AWS Certified Machine Learning, Google Cloud Professional ML Engineer).

• Experience with generative AI, reinforcement learning, or advanced NLP techniques.

• Contributions to open-source AI projects or publications in AI/ML conferences/journals.

• Familiarity with agile methodologies and DevOps practices.