Senior ML Engineer – LLMs and RAG Systems

3 weeks ago

Lahore, Punjab, Pakistan Arbisoft Full time

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)
  • Develop robust and scalable RAG pipelines. Integrate 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:

  • 3+ 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
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