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Join Data Eminence as a remote Machine Learning Engineer and help develop intelligent systems that solve real-world problems. You'll work with data, machine learning models, algorithms, and deployment tools to build practical AI solutions. Ideal for entry-level candidates with strong Python and machine learning fundamentals.
Full Description
Data Eminence is seeking a motivated Machine Learning Engineer to join our growing AI and data team. In this role, you will support the development, training, evaluation, and deployment of machine learning models.
You will collaborate with data scientists, software engineers, and product teams to transform business requirements and datasets into useful machine learning solutions.
Your key responsibilities will include:
We believe in learning by doing. You'll gain hands-on experience with Python, NumPy, Pandas, scikit-learn, PyTorch or TensorFlow, Jupyter, REST APIs, Git, and cloud-based machine learning platforms.
Requirements
Benefits
Job ID: 153894139
Skills:
Pytorch, ASR, TTS, Hugging Face, low-resource language modeling, LLM orchestration, intent
Skills:
Microservices, Python, BigQuery, Computer Vision, Google Cloud Platform, Automated Testing, MLops, Authentication, Prompt engineering, Progressive deployment, Entity resolution, Hybrid retrieval, Cloud Run, semantic search, Vertex AI, Validation gates, Error handling, Data pipelines, Keyword search, Agentic workflows, Knowledge graph, Production APIs, Generative AI, Relationship discovery, Model versioning, Vector embeddings, CI/CD pipelines, Retrieval-augmented generation, Multi-agent orchestration
Skills:
Java, Machine Learning, Scala, Kafka, Redshift, Redis, Sql, Tensorflow, Sklearn, Rabbitmq, Jenkins, Docker, Pytorch, Spark, Flask, FastAPI, MongoDB, Python, Kubernetes, Airflow, Sagemaker, Milvus, MLFlow
Skills:
MLops, Cloud deployment, Python, responsible AI, evaluation drift, ML fundamentals, modern ML tooling
Skills:
Pytorch, Python, LangChain, orchestration frameworks, vector search, LLMs, RAG systems, high-availability inference systems, scalable ML architecture, semantic embeddings, LlamaIndex