AI/LLM engineer

4 weeks ago

Baddi, India Latent Full time

Job Title: AI/LLM EngineerLocation: RemoteExperience: 3+ yearsEmployment Type: Full-timeAbout UsAt Latent, we are on a mission to push the boundaries of AI by building cutting-edge solutions powered by Large Language Models (LLMs). We are looking for a passionate AI/LLM Engineer who thrives in working with state-of-the-art NLP, Deep Learning, and Transformer models to develop and deploy AI-driven applications.Role OverviewAs an AI/LLM Engineer, you will be responsible for training, fine-tuning, optimizing, and deploying LLMs for real-world applications. You will work with large-scale datasets, leverage cloud platforms, and collaborate with cross-functional teams to develop scalable and efficient AI solutions.Key ResponsibilitiesTrain and fine-tune LLMs on domain-specific datasets.Optimize models for scalability, performance, and cost-efficiency.Deploy models on cloud platforms (AWS, GCP, Azure) and integrate them into applications.Work with APIs, vector databases (FAISS, Pinecone, Weaviate), and retrieval-augmented generation (RAG) techniques.Implement LLM inference optimization strategies to enhance speed and accuracy.Research and apply cutting-edge techniques in NLP and Deep Learning.Collaborate with cross-functional teams, including software engineers, data scientists, and product managers.Requirements3+ years of experience in AI/ML, focusing on NLP and Large Language Models.Strong proficiency in Python and deep learning frameworks like PyTorch, TensorFlow, or JAX.Hands-on experience with Hugging Face, OpenAI, LangChain, or similar frameworks.Expertise in LLM training, fine-tuning, and inference optimization.Experience with cloud-based model deployment and APIs.Familiarity with vector databases, embedding models, and knowledge retrieval techniques.Strong understanding of Transformer architectures, attention mechanisms, and Deep Learning principles.Preferred Skills (Nice to Have)Experience with distributed training and model parallelism.Knowledge of low-latency inference optimization techniques (e.g., quantization, distillation).Familiarity with MLOps, CI/CD for ML models, and Kubernetes for AI workloads.

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