Senior Machine Learning Engineer

Brickstech; Lahore; On-site; PKR 2,400,000 - 4,800,000; Full time

Job summary

Brickstech is seeking a Senior Machine Engineer with around 5 years of hands-on experience building, shipping, and maintaining production software, and deploying cutting-edge AI solutions. The role emphasizes infrastructure, CI/CD, and platform reliability.

The candidate will own deployment configurations for Docker Swarm and Kubernetes, work with SQL data processing, and contribute to scalable RAG pipelines using embeddings in FAISS, Pinecone, or Weaviate, while collaborating with engineers and

Qualifications

• BS in CS or related field; MS preferred.
• 5 years of hands-on production software experience.
• Experience deploying AI systems at scale.
• Strong DevOps practices and infrastructure automation.

Responsibilities

• Build and maintain CI/CD pipelines and deployment configurations.
• Design scalable deployment for Docker Swarm and Kubernetes.
• Evaluate ML/DL models using PyTorch, TensorFlow, or similar frameworks.
• Develop and optimize LLM-based systems including LoRA/QLoRA techniques.
• Own infrastructure automation and monitoring across environments.
• Write tests and build scalable testing frameworks.

Skills

Python

CI/CD pipelines

GitHub Actions

Docker & Kubernetes

ML/AI systems

Communication with stakeholders

LLMs

Education

BS in Computer Science or related field

Masters preferred

Tools

Docker Swarm

Kubernetes

FAISS

Pinecone

Weaviate

PyTorch

TensorFlow

Job description

Job Description

We're looking for a Senior Machine Engineer with around 5 years of hands-on experience building, shipping, and maintaining production software, and deploying cutting-edge AI solutions. The ideal candidate brings deep expertise in Python, transformers, and scalable cloud-based ML systems. This is an engineering-first role, the majority of your time will go into infrastructure, CI/CD, and platform reliability.

What you'll be doing

• Building and maintaining CI/CD pipelines using GitHub Actions
  
• Designing and managing deployment configurations for Docker Swarm and Kubernetes,
  
• 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).
  
• Owning infrastructure automation and applying solid DevOps practices across environments
  
• Setting up and maintaining monitoring for services running in production
  
• Writing automated tests and building testing frameworks that scale with the codebase
  
• Troubleshooting and maintaining complex, sometimes messy codebases and improving them
  
• Actively identifying and resolving technical debt
  
• Working with SQL and handling data processing tasks as needed
  
• Occasionally training simple ML models (e.g. regression models in PyTorch)
  
• Can develop robust and scalable RAG pipelines. In-depth knowledge of embeddings and can work with vector databases like FAISS, Pinecone, Weaviate, etc.
  
• Communicating clearly and consistently with engineers, data scientists, product managers, and business stakeholders
  

What we're looking for

• 5 years of industry experience building and shipping software
  
• BS in Computer Science or masters
  
• Strong command of CI/CD workflows, GitHub Actions, Docker, and infrastructure automation
  
• Solid experience with Kubernetes and Docker Swarm deployment setups
  
• Good working knowledge of SQL and data processing
  
• Strong code engineering fundamentals, can read, debug, and maintain codebases you didn't write
  
• A track record of dealing with tech debt, not just avoiding it
  
• Excellent communication skills who can explain technical tradeoffs to non-technical stakeholders and stay aligned with cross-functional teams over time
  

Communication & Ownership Expectations

• Understand the task fully and clarify uncertainties upfront
  
• Create and maintain a clear ticket, aligned with the team on scope
  
• Set an estimate once scoped
  
• Flag blockers early and communicate delays as soon as they arise
  
• Let the team know if you finish early
  
• Share at least a weekly update, more often when there are significant changes