Senior ML engineer
3 weeks ago
What's in it for you:
- Be a part of a global company and build solutions at enterprise scale
- Work with a highly skilled and hands-on technical team (including leadership)
- Contribute to solving high complexity, high impact problems
- Build and maintain production ready pipelines from ideation to deployment
Responsibilities:
- Design, build, and deploy scalable machine learning models for information extraction and natural language processing.
- Develop end-to-end ML pipelines, from data preprocessing and feature engineering to model training, evaluation, and deployment.
- Work with large-scale data processing frameworks like Apache Spark to build efficient and optimized ML solutions.
- Optimize ML models for performance, accuracy, and scalability in production environments.
- Develop and maintain streaming and batch data transformation pipelines using cloud-native solutions (AWS/GCP).
- Implement best practices in MLOps, including CI/CD pipelines for ML models, automated testing, and model monitoring.
- Deploy and manage containerized ML applications using Docker and Kubernetes.
- Collaborate closely with data engineers, software developers, and product teams to integrate ML solutions into enterprise workflows.
- Stay up to date with emerging trends in machine learning, NLP, and AI, applying new techniques to improve existing models.
- Ensure high code quality, performance, and adherence to best practices for software development.
What We're Looking For:
- 5-8 years of experience in Machine Learning, NLP, and MLOps or related technology.
- Strong expertise in Python and ML/NLP libraries (e.g., spaCy, NLTK, Transformers, Hugging Face).
- Experience with LangChain for LLM-based applications (retrieval-augmented generation, prompt engineering, and AI-driven search).
- Experience working with distributed computing frameworks such as Apache Spark for large-scale data processing.
- Hands-on experience in cloud-based ML development (AWS/GCP).
- Strong understanding of data preprocessing, feature engineering, model training, and evaluation.
- Experience with MLOps best practices, including automated model deployment and monitoring.
- Proficiency in SQL (any variant, big data experience is a plus).
- Familiarity with Linux OS (bash scripting, utilities, etc.).
- Version control experience with Git, GitHub, or Azure DevOps.
- Strong problem-solving, debugging, and optimization skills.
Nice to Have:
- Experience with Core Java 17+ (preferably Java 21+) and its toolchain.
- Knowledge of Apache Kafka and Apache Avro.
- Exposure to JVM-based languages such as Kotlin or Scala.
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