AI/ML Engineer

JD for AI ML Engineer

Role and Responsibilities
● Design, develop, and deploy advanced AI models with a focus on generative AI,
including transformer architectures (e.g., GPT, BERT, T5) and other deep learning
models used for text, image, or multimodal generation.
● Work with extensive and complex datasets, performing tasks such as cleaning,
preprocessing, and transforming data to meet quality and relevance standards for
generative model training.
● Collaborate with cross-functional teams (e.g., product, engineering, data science) to
identify project objectives and create solutions using generative AI tailored to business
needs.
● Implement, fine-tune, and scale generative AI models in production environments,
ensuring robust model performance and efficient resource utilization.
● Develop pipelines and frameworks for efficient data ingestion, model training, evaluation,
and deployment, including A/B testing and monitoring of generative models in
production.
● Stay informed about the latest advancements in generative AI research, techniques, and
tools, applying new findings to improve model performance, usability, and scalability.
● Document and communicate technical specifications, algorithms, and project outcomes
to technical and non-technical stakeholders, with an emphasis on explain ability and
responsible AI practices.

Qualifications Required
● Educational Background: Bachelors or Masters degree in Computer Science, Data
Science, AI/ML, or a related field. Relevant Ph.D. or research experience in generative
AI is a plus.
● Experience: 410 years of experience in machine learning, with 2+ years in designing
and implementing generative AI models or working specifically with transformer-based
models.

● Technical Expertise:
○ Proficiency in Python, along with experience in libraries and frameworks central
to generative AI, such as Hugging Face Transformers, PyTorch, and TensorFlow.
○ Strong understanding of transformer architectures, language models, and
generative modeling techniques (e.g., GANs, VAEs, autoregressive models).
○ Expertise in data processing techniques for training large language models,
including handling unstructured data, tokenization, and feature extraction.
○ Familiarity with MLOps practices and tools (e.g., Docker, Kubernetes, MLflow) for
deploying and managing large-scale models in production.

○ Experience with cloud platforms (AWS, GCP, Azure) and GPU/TPU resources for
training and fine-tuning large models.

● Core Skills:
○ Machine Learning: Strong foundation in machine learning algorithms, deep
learning, generative AI techniques.
○ Programming: Proficient in Python, with knowledge of SQL for data handling
and retrieval.
○ Data Engineering: Experience with data preprocessing, feature engineering, and
data transformation specific to large and complex datasets.
○ Model Evaluation: Knowledge of model evaluation metrics and techniques for
generative models, especially for text generation, image synthesis, or multimodal
AI.

● Soft Skills:
○ Strong analytical and problem-solving skills with a high level of attention to detail.
○ Excellent communication skills, with the ability to explain complex generative AI
concepts to both technical and non-technical audiences.
○ Collaborative mindset with the capability to work effectively in cross-functional
teams.

Skills and Experience Required
● Generative AI: Transformer Models, GANs, VAEs, Text Generation, Image Generation
● Machine Learning: Algorithms, Deep Learning, Neural Networks
● Programming: Python, SQL; familiarity with libraries such as Hugging Face
Transformers, PyTorch, TensorFlow
● MLOps: Docker, Kubernetes, MLflow, Cloud Platforms (AWS, GCP, Azure)
● Data Engineering: Data Preprocessing, Feature Engineering, Data Cleaning