AI/ML Architect

Ignatiuz is a digital transformation and intelligent workplace consulting company with offices in the US (PA) and India (Indore).

Focused on accelerating digital performance through innovation and automation.

Our team has worked with a variety of Fortune 500 clients and has a track record of delivering reliable, high-quality solutions that drive business success.

• Lead the design and development of robust machine learning and deep learning architectures tailored to solve real-world business challenges.
• Build and optimize models using frameworks like TensorFlow, PyTorch, and Keras, ensuring high accuracy and performance.
• Architect and oversee end-to-end data pipelines, including data ingestion, preprocessing, and feature engineering for AI workloads.
• Research, prototype, and productionize novel algorithms in areas such as computer vision, NLP, and reinforcement learning.
• Deploy models using cloud platforms (AWS/Azure/GCP), leveraging GPU/TPU infrastructure, and integrating into CI/CD workflows using Docker and other tools.
• Tune hyperparameters, manage model drift, and implement monitoring to ensure sustained model performance in production.
• Collaborate with engineering, data science, and product teams to align AI initiatives with business goals.
• Ensure robust documentation, version control, and adherence to MLOps principles throughout the development lifecycle.
• Stay ahead of trends in AI/ML and guide the team on best practices, tools, and frameworks.

Required Skills and Qualifications:

• Education:

   Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or a related technical field.

• Experience:

   4–5 years of experience in developing and deploying machine learning or deep learning solutions in real-world environments.

• Technical Expertise:

   Proficient in Python and ML libraries: NumPy, Pandas, Scikit-learn, Matplotlib, etc. Deep experience with TensorFlow, PyTorch, and Keras. Strong grasp of neural networks, CNNs, RNNs, Transformers, and GANs. Experience with GPU/TPU acceleration, model optimization, and distributed training. Familiarity with cloud platforms (AWS, Azure, or GCP) for scalable AI deployments. Hands-on with Docker, version control, and CI/CD tools for deployment automation. Experience in NLP, Computer Vision, or Reinforcement Learning projects is a strong plus.

• Preferred Traits:

   Strong problem-solving and architectural thinking Ability to work in agile, cross-functional teams Passion for innovation and applying AI to business use cases