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 solid 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. (ref:hirist.tech)

📌 Cognite - Machine Learning Engineer - NLP/Computer Vision (Bengaluru)
🏢 COGNITE
📍 Bengaluru