Senior ML Engineer Infrastructure & Data Systems

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

Once a task is assigned, you're expected to own it end-to-end:

• 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