Senior ML Engineer: LLMs, RAG & Scalable AI

Taraki; Lahore; On-site; PKR 2,000,000 - 3,500,000; Full time

Benefits offered by this job

Dockerized workflows

Job summary

Arbisoft is seeking a Senior ML Engineer to design and deploy cutting-edge AI solutions, including LLMs, RAG pipelines, and agentic workflows. The role emphasizes Python, transformers, and scalable cloud ML systems.

You will collaborate with research and product teams to translate ideas into production-grade ML features with latency and throughput optimizations in cloud environments.

Qualifications

• 5+ years of experience in machine learning and deep learning, including building models from scratch.
• 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 and agent-based workflows; familiarity with LangChain/LlamaIndex.

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.
• Develop robust and scalable RAG pipelines and embeddings with vector databases.
• Construct and maintain Agentic AI workflows with reasoning, tool calls, memory, and planning.
• Collaborate with research and product teams to productionise ML features.

Skills

Python

Transformers

LLMs

RAG pipelines

Cloud ML

OOP & testing

Model deployment

Production ML

Tools

Docker

Kubernetes

Airflow

FAISS

Pinecone

Weaviate

LangChain

LlamaIndex

TensorRT

TorchServe

Job description

Job Openings Machine Learning (ML) Engineer (Senior) - Arbisoft

About the job Machine Learning (ML) Engineer (Senior) - Arbisoft

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.