Machine Learning (ML) Engineer (Senior) - Arbisoft at Taraki – Lahore | Jobyssey

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.

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