Our client Arbisoft is looking for Senior ML Engineer in Lahore. Job Description: Arbisoft is looking for an experienced ML Engineer to design and deploy cutting-edge AI solutions, including LLMs, RAG pipelines, and agentic workflows. The ideal candidate brings deep expertise in Python, transformers, and scalable cloud-based ML systems. Key Responsibilities: - Design, implement, and evaluate ML/DL models using PyTorch, TensorFlow, or similar frameworks. - Build and optimize LLM-based systems, including prompt-tuning, fine-tuning, and adapter-based training (e.g., LoRA, QLoRA). - Can develop robust and scalable RAG pipelines. In-depth knowledge of embeddings and can work with vector databases like FAISS, Pinecone, Weaviate, etc. - Construct and maintain Agentic AI workflows involving multi-step reasoning, tool calling, memory components, and planning logic. - Work with Proprietary APIs, as well as open-source libraries and models - Develop modular and clean Python code, adhering to software engineering best practices (OOP, reusable components, testing). - Implement scalable solutions in cloud environments (like AWS), leveraging GPU/TPU resources effectively. - Design inference pipelines that are robust and optimized for latency and throughput. - Collaborate with research and product teams to translate ideas into production-grade ML features. Required Skills: - 5+ years of experience in machine learning and deep learning, including building models from scratch. - Has a track record of shipping ML solutions that scale in production. - Strong proficiency in Python and deep understanding of software design principles. - Proven experience with transformer-based architectures, LLMs, and embedding models. - Hands-on experience with RAG systems, deep understanding of agent-based systems. Familiarity with LangChain, LlamaIndex, or similar frameworks. - Experience with cloud platforms (AWS/GCP/Azure) and understanding of scalability, resource optimization, and model deployment. - Familiarity with performance profiling, efficient model serving, and hardware-aware design (e.g., GPU utilization, quantization). - Ability to read, debug, and contribute to complex ML/DL codebases. Good to have: - Experience with MLOps, orchestration tools (e.g., Airflow, AWS Step Functions), containerization (Docker, Kubernetes). - Exposure to optimization toolkits (ONNX, TensorRT) and serving frameworks (Triton, TorchServe). - Experience with experiment tracking (e.g., Weights & Biases, Comet). - Understanding of alignment techniques like RLHF or curriculum learning.