Job Summary:

We are looking for a talented and innovative AI Engineer to join our team. The ideal candidate will design, develop, and deploy advanced artificial intelligence solutions that enhance business operations, improve customer experience, and drive innovation. This role requires strong programming skills, a deep understanding of machine learning and deep learning algorithms, and the ability to work collaboratively with data scientists, software engineers, and product teams to build scalable AI applications that solve complex business challenges. This role demands expertise in state-of-the-art AI technologies, deep learning architectures, and hands-on experience with generative models such as GPT, Transformers, VAEs, or GANs.

Key Responsibilities

• Design, develop, and implement AI models and algorithms for predictive, prescriptive, and generative use cases to solve complex business problems.
• Build and optimize machine learning pipelines from large-scale data preprocessing to model deployment.
• Collect, clean, and preprocess structured and unstructured data for model training. Ensure data integrity and compliance with security and privacy standards.
• Train, validate, and fine-tune models using frameworks like TensorFlow, PyTorch, or Hugging Face.
• Deploy models into production using MLOps tools (e.g., MLflow, Docker, Kubernetes).
• Integrate AI solutions with enterprise applications and APIs.
• Design scalable architectures for AI systems, ensuring high availability and performance.
• Implement observability, monitoring, and A/B testing for deployed models.
• Continuously improve models based on feedback and performance metrics.
• Work closely with data scientists, software engineers, and business stakeholders to align AI initiatives with organizational goals.
• Stay updated on emerging AI trends (LLMs, RAG systems, multimodal AI) and recommend improvements.
• Design and develop generative AI models tailored to solve business challenges, including natural language generation, image synthesis, code generation, or other AI-generated content.
• Implement, fine-tune, and optimize state-of-the-art transformer architectures and other deep learning frameworks.
• Collaborate closely with data scientists, AI researchers, and product teams to integrate generative AI capabilities into scalable applications and platforms.
• Build and maintain robust AI pipelines for training, evaluation, and deployment of GenAI models in cloud or on-premises environments.
• Research and experiment with the latest generative AI advancements to incorporate new methodologies and improve model performance.
• Develop APIs and user-facing tools that leverage generative AI for automation, content creation, personalization, or decision support.
• Ensure data privacy, ethical AI usage, and compliance with applicable regulations in model development and deployment.
• Document models, code, and workflows to support maintainability and knowledge sharing across teams.

Required Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related field.
• Proven 5-8 years of experience in developing and deploying generative AI models (e.g., GPT, BERT, GANs, VAEs).
• Strong proficiency in Python and AI/ML frameworks such as TensorFlow, PyTorch, or JAX.
• Hands-on experience with NLP, computer vision, or multimodal generative AI applications.
• Familiarity with cloud platforms (AWS, Azure, GCP) and containerization tools (Docker, Kubernetes) for scalable AI model deployment.
• Solid understanding of machine learning pipeline development, data preprocessing, and model evaluation metrics.
• Ability to work in an agile environment and collaborate effectively with cross-functional teams.

• Proficiency in Python, Java, or C++.
• Strong knowledge of machine learning, deep learning, NLP, and computer vision.
• Experience with LLMs (e.g., GPT, LLaMA) and Generative AI and Tools & Frameworks such as TensorFlow, PyTorch, Scikit-learn, LangChain, Docker, Kubernetes.

Preferred Skills

• Experience with prompt engineering and fine-tuning large language models.
• Knowledge of reinforcement learning, few-shot learning, or transfer learning techniques.
• Background in software engineering best practices, including version control and CI/CD pipelines.
• Familiarity with AI ethics, bias mitigation, and fairness in generative models.
• Exposure to data engineering, big data tools, or MLOps frameworks.

• Strong problem-solving and analytical skills.
• Familiarity with ethical AI principles and responsible AI practices.
• Excellent communication and collaboration abilities.

What We Offer

• Opportunity to work on pioneering AI technologies with real-world impact.
• Collaborative and inclusive work environment fostering innovation.
• Access to state-of-the-art computing resources and learning opportunities.