We are looking for:

Results-driven AI/ML Engineer with expertise in designing, training, and deploying scalable machine learning and deep learning models. Skilled in Python, TensorFlow, and cloud-based AI solutions, with strong experience in data preprocessing, model optimization, and MLOps integration and Generative AI. Adept at translating complex business challenges into AI-driven insights and automation.

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 data sets, 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

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Qualifications Required

Educational Background:

• Bachelor’s or master’s 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:

• 3+ 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 ML Ops practices and tools (e.g., Docker, Kubernetes, ML flow) 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

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You would enjoy

• Opportunity to work on impactful technical challenges with global reach.
• Vast opportunities for self-development, including online university access and knowledge sharing opportunities.
• Sponsored Tech Talks & Hackathons to foster innovation and learning.
• Generous benefits packages including health insurance, retirement benefits, flexible work hours, and more.
• Supportive work environment with forums to explore passions beyond work.

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