What You'll Work On
As a Junior Machine Learning Engineer, you will:
- Assist in the design, development, and deployment of machine learning models and pipelines.
- Support the automation of training, evaluation, and deployment workflows.
- Contribute to the implementation of MLOps best practices including versioning, monitoring, and model management.
- Work with tools like Docker, MLflow, Weights & Biases, or similar for reproducible experimentation.
- Collaborate with data scientists, backend engineers, and product teams to deliver production-ready ML solutions.
- Learn to deploy models in cloud environments such as AWS, GCP, or Azure.
- Stay up to date with machine learning advancements and apply relevant techniques to improve performance and scalability.
Core Skills & Qualifications
We are looking for someone with:
- 1+ years of experience in machine learning or data science roles.
- Proficient in Python and popular ML libraries like Scikit-learn, Pandas, TensorFlow, or PyTorch.
- Solid understanding of ML concepts such as supervised/unsupervised learning, feature engineering, and evaluation metrics.
- Basic experience working with SQL or NoSQL databases.
- Strong understanding of data structures and algorithms.
- Familiarity with API integration and deploying ML models in production environments is a plus.
- Exposure to cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes) is a plus.
- Experience with tools like MLflow, LangChain, LangSmith, or vector databases is a bonus.
- Good communication skills, a growth mindset, and an eagerness to learn.
Nice-to-Have
- Experience with building and maintaining Retrieval-Augmented Generation (RAG) pipelines, leveraging vector databases and retrieval techniques to enhance LLM-powered applications.
- Familiarity with distributed computing and big data technologies.
- Contributions to open-source ML projects or research publications.
Job Type: Full-time
Education:
- Bachelor's (Required)
Experience:
- machine learning or data science roles: 2 years (Required)
- Python: 1 year (Required)
- ML libraries like Scikit-learn, Pandas, TensorFlow, PyTorch.: 1 year (Required)
- SQL or NoSQL databases: 1 year (Required)
- data structures and algorithms: 1 year (Required)
- API integration and deploying ML models: 1 year (Required)
- cloud platforms (AWS, GCP, or Azure) : 1 year (Required)
- containerization tools (Docker, Kubernetes): 1 year (Preferred)
- MLflow, LangChain, LangSmith: 1 year (Preferred)
- vector databases: 1 year (Preferred)
Work Location: Remote
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