Role Overview :

As a Machine Learning Engineer, you will be a key contributor to building the models and pipelines that power our industrial contextualization platform. Working closely with senior and staff-level engineers, you will focus on execution - transforming research prototypes into high-performance, real-time applications and building the infrastructure that supports them.

This is an engineering-first role. We seek hands-on builders who write production-grade code, understand system design, and tackle complex infrastructure, treating ML models as software components in a large industrial architecture - not pure data scientists building isolated models.

Ownership :

- You love writing clean, readable code and seeing it work.

- You are deeply curious about how complex ML systems operate in the real world and are passionate about turning raw data into robust, reliable pipelines.

- You don't just write research scripts; you want to build durable software.

- You know how to take a well-defined technical plan, execute it efficiently, and ask the right questions when you hit roadblocks.

- You prioritise building things the right way the first time over quick, fragile hacks.

- You are eager to absorb new paradigms in MLOps, containerization, and model deployment, applying feedback rapidly to improve your craft.

Responsibilities :

- Build, test, and maintain robust data pipelines for large-scale industrial datasets.

- Assist in deploying data science prototypes to production.

- Write high-quality, testable Python code, primarily creating RESTful/gRPC APIs for ML capabilities.

- Containerise applications (Docker) and navigate Linux to handle massive volumes of unstructured industrial data.

- Run experiments, fine-tune existing foundational models (NLP, Vision-Language), and evaluate ML libraries to enhance document parsing and entity matching.

- Write rigorous unit/integration tests and monitor deployed models for drift and performance.

- Collaborate closely with senior staff (engineers, data scientists, product managers) to deliver features on the technical roadmap.

Requirements :

- Bachelor's or Master's degree in Computer Science, Data Science, Software Engineering, or a related field.

- 3 - 6 years of industry experience in software engineering with a strong focus on machine learning, or vice versa.

- Strong programming skills in Python, with a proven ability to write clean, modular, and testable code.

- Solid hands-on experience with frameworks like PyTorch, TensorFlow, Hugging Face, or LangChain.

- Experience working with large datasets, writing complex SQL queries, and hands-on familiarity with data processing libraries or frameworks (e.g., PySpark, Pandas at scale, or Dask).

- Familiarity with MLOps basics, containerization (Docker), Linux command-line tools, and fundamental cloud services (AWS, Azure, or GCP).

- Solid understanding of core computer science fundamentals, data structures, and algorithms.

What Sets Your Role Apart :

- The ability to take a well-defined technical problem or architecture plan from a principal engineer and write highly efficient, bug-free code to solve it.

- A rapid trajectory in absorbing new paradigms (like vector databases or agentic workflows) and efficiently applying them to massive volumes of real-world industrial data without crashing the system.

- A commitment to treating ML code like production software, never skipping the tests or the documentation.

Preferred Qualifications :

- Academic or early industry experience with Vision-Language Models (VLMs) or Computer Vision (e.g., document layout analysis).

- Hands-on familiarity with basic RAG pipelines and Vector Databases (e.g., Pinecone, Milvus, Qdrant).

- Exposure to agentic frameworks (e.g., LangGraph).

- Exposure to modern columnar data (e.g., Parquet) or data lakes (e.g., Delta Lake, Apache Iceberg).

- Understand efficient querying/chunking of large unstructured data for ML.

- Experience with highly unstructured data, including text and JSON extraction from messy PDFs using OCR. OT dataset exposure is a plus.

- Experience serving models via backend web frameworks (FastAPI/Flask).

- Comfortable troubleshooting Linux permissions, fonts, and dependencies.