JD: AI ML senior software professional / architect

JD of: AI/ML Architect with expertise in Generative AI to develop and deploy advanced machine learning models that solve real-world business challenges. The ideal candidate should have a strong understanding of transformer architectures, a hands-on approach to handling large datasets, and the ability to scale solutions in production environments.

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Responsibilities

Model Development:

Build and fine-tune generative AI models such as GPT, BERT, T5, GANs, VAEs, and autoregressive models.

Experiment with multimodal AI models combining text, images, and other data modalities.

Data Handling and Preprocessing:

Collect, clean, preprocess, and transform large and complex datasets for model training.

Perform tokenization, text normalization, and feature extraction for unstructured data.

Automate data preprocessing pipelines using Python scripts or ETL frameworks.

Model Optimization and Deployment:

Implement techniques like transfer learning, fine-tuning, and hyperparameter optimization to improve model performance.

Deploy models to production environments using Docker and Kubernetes for containerization and scaling.

Use MLflow, Airflow, or equivalent tools for managing machine learning workflows and versioning.

Optimize deployment on cloud platforms (AWS, GCP, or Azure) with GPU/TPU support.

System Architecture and Pipelines:

Design scalable pipelines for:

Data ingestion.

Model training and evaluation.

A/B testing and continuous monitoring.

Implement CI/CD pipelines for machine learning.

Research and Innovation:

Explore the latest advancements in generative AI and apply new techniques like LoRA (Low-Rank Adaptation), RLHF (Reinforcement Learning with Human Feedback), and sparse transformers.

Conduct PoCs (Proof of Concepts) to test new model architectures and methodologies.

Collaboration and Communication:

Work with cross-functional teams (product, engineering, and data science) to align AI capabilities with business goals.

Explain technical concepts and outcomes effectively to both technical and non-technical stakeholders.

Technical Skills

Programming:

Expert-level Python knowledge, with experience in libraries like:

Hugging Face Transformers, PyTorch, TensorFlow.

Pandas, NumPy for data manipulation.

Matplotlib, Seaborn for visualizations.

Familiarity with SQL for querying large datasets.

Generative AI Models:

Hands-on experience building transformer-based models like GPT, BERT, and T5.

Knowledge of diffusion models, autoencoders, and GANs for image and multimodal generation.

Data Engineering:

Proficiency in preprocessing large datasets: removing noise, handling missing values, and creating embeddings.

Familiarity with tools like Apache Spark, Dask, or Hadoop for processing massive datasets.

Machine Learning Operations (MLOps):

Experience with tools such as Docker, Kubernetes, and MLflow.

Exposure to distributed training frameworks like Horovod or Ray.

Cloud Platforms:

Proven experience using AWS (SageMaker, S3), GCP (Vertex AI), or Azure (Machine Learning Studio).

Expertise in leveraging GPUs and TPUs for training models.

Evaluation Metrics:

Knowledge of metrics like BLEU, ROUGE, FID (Fréchet Inception Distance), perplexity, and accuracy for evaluating generative models.

Soft Skills

Problem-Solving: Ability to break down complex problems into actionable solutions.

Teamwork: A strong collaborator who thrives in cross-functional environments.

Communication: Capability to explain algorithms and project results in a clear and engaging manner.

Qualifications

Education: Bachelor’s/Master’s in Computer Science, AI/ML, Data Science, or a related field. A Ph.D. or substantial research experience in Generative AI is a plus.

Experience:

10-15 years in machine learning.

At least 5+ years specializing in Generative AI or transformer-based architectures.

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How to reach this level from different backgrounds: https://healthtech2000.blogspot.com/2024/11/career-routes-and-examples-of.html