Senior AI Engineer

· Train, fine-tune, and deploy Large Language Models (LLMs) to solve real-world problems effectively.

· Design, implement, and optimize AI/ML pipelines to support model development, evaluation, and deployment.

· Collaborate with Architect, software engineers, and product teams to integrate AI solutions into applications.

· Ensure model performance, scalability, and efficiency through continuous experimentation and improvements.

· Work on LLM optimization techniques, including Retrieval-Augmented Generation (RAG), prompt tuning, etc.

· Manage and automate the infrastructure necessary for AI/ML workloads while keeping the focus on model development.

· Work with DevOps teams to ensure smooth deployment and monitoring of AI models in production.

· Stay updated on the latest advancements in AI, LLMs, and deep learning to drive innovation.

· Strong experience in training, fine-tuning, and deploying LLMs using frameworks like PyTorch, TensorFlow, or Hugging Face Transformers.

· Hands-on experience in developing and optimizing AI/ML pipelines, from data preprocessing to model inference.

· Solid programming skills in Python and familiarity with libraries like NumPy, Pandas, and Scikit-learn.

· Strong understanding of tokenization, embeddings, and prompt engineering for LLM-based applications.

· Hands-on experience in building and optimizing RAG pipelines using vector databases (FAISS, Pinecone, Weaviate, or ChromaDB).

· Experience with cloud-based AI infrastructure (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).

· Experience in model monitoring, A/B testing, and performance optimization in a production environment.

· Familiarity with MLOps best practices and tools (Kubeflow, MLflow, or similar).

· Ability to balance hands-on AI development with necessary infrastructure management.

· Strong problem-solving skills, teamwork, and a passion for building AI-driven solutions.