Machine Learning Engineer
Cognite · Bengaluru · onsite · Posted 2d ago
Undisclosed3–6 yrsfulltimeSourced from
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About this role
Job Summary
About the Role
Key Responsibilities
Required Skills and Qualifications
What Sets You Apart
Preferred Qualifications
We are building the next generation of contextual AI for Industrial Operations. Our team focuses on transforming unstructured, complex industrial data-ranging from technical manuals to complex piping and instrumentation diagrams (PIDs)-into structured, actionable intelligence. We leverage state-of-the-art Deep Learning, Generative AI, and Computer Vision to drive efficiency, safety, and operational excellence.
About the Role
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.
Key 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.
- Containerize 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.
Required Skills and Qualifications
- 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 You 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.
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Skills
DockerCraftdaskcloud servicesapisApacheComputer Visionamazon web servicesData StructuresdeploymentcloudDigital TransformationAlgorithmsArchitectureDeep LearningData ProcessingMicrosoft AzureData PipelineData ScienceArtificial Intelligence
About Cognite
Cognite
Emerging Technologies
Lysaker
SK
About
Cognite operates in industrial software, utilizing AI to enhance production efficiency for energy and manufacturing sectors by optimizing data accessibility and usage.
Industries
Emerging Technologies
Employee ratings
6 reviews
2.0/ 5
Culture
1.7
Career growth
1.4
Work-life
2.2
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