Gen AI Developer

Job Title: Generative AI Engineer

Job Overview:

We are on the lookout for a creative and technically proficient Generative AI Engineer to join our team. This individual will be responsible for architecting and deploying innovative AI systems centered around large language models (LLMs). The role emphasizes hands-on development in areas like prompt engineering, model customization, and real-world implementation of generative AI capabilities.

As part of the team, you will help design and build applications such as AI chat interfaces, intelligent content generators, summarization engines, and other tools that utilize state-of-the-art LLM technologies.

Key Responsibilities:

• Build, train, and implement generative AI systems using models such as GPT, BERT, LLaMA, Claude, or similar.
• Adapt and fine-tune pre-trained foundation models to meet specific business needs and domains.
• Integrate generative AI into various products, including conversational agents, summarization tools, and AI content creators.
• Conduct exploratory research to remain updated on the latest advancements in LLMs, prompt optimization, and multi-modal AI technologies.
• Prepare and manage relevant datasets for model training, evaluation, and testing.
• Collaborate with teams from data science, engineering, and product development to ensure smooth integration of AI models into live systems.
• Monitor model behavior continuously and make improvements to ensure high accuracy, low latency, and consistent reliability.
• Apply responsible AI principles and set up feedback mechanisms to refine model output over time.

Required Skills and Qualifications:

• A Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or a related technical discipline.
• Proven experience in applying LLMs and generative AI techniques in production environments.
• Strong programming skills in Python and familiarity with machine learning libraries such as PyTorch, TensorFlow, and Hugging Face Transformers.
• Solid grounding in NLP methods, including text generation, translation, summarization, and conversational AI.
• Hands-on experience with techniques like prompt tuning, LoRA, PEFT, and RLHF for fine-tuning models.
• Understanding of cloud-based deployment using platforms such as AWS, GCP, or Azure.
• Strong analytical and debugging skills, with attention to performance tuning and cost optimization.
• Knowledge of MLOps best practices, including model version control (e.g., MLflow, Git), and container technologies like Docker and Kubernetes is advantageous.