Responsibilities:
Data & Model Development
- Collect, clean, and preprocess data for training and testing.
- Design and develop machine learning models (classical ML or deep learning).
- Train, tune, and validate models using real-world datasets.
- Conduct performance testing and error analysis.
System Integration
- Develop APIs or services to integrate the model into the main product or platform.
- Collaborate with backend and frontend developers to embed AI functionality.
- Ensure efficient inference and scalability in production environments.
MLOps & Deployment
- Package and deploy models (e.g., via Docker, FastAPI, or cloud ML services).
- Set up monitoring for model accuracy, drift, and system performance.
- Maintain version control for models and data pipelines.
Collaboration & Documentation
- Work closely with the Product Manager to translate business goals into technical solutions.
- Document models, datasets, and architecture decisions.
- Communicate findings and results clearly to non-technical stakeholders.
Qualifications:
- Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or a related field.
- 3+ years of experience in applied machine learning or AI engineering.
- Strong proficiency in Python and frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Experience building and deploying APIs (e.g., FastAPI, Flask, Node).
- Familiarity with cloud services (AWS, GCP, Azure) or containerization (Docker, Kubernetes).
- Practical knowledge of data pipelines (ETL, data versioning, labeling tools).
- Strong understanding of model evaluation and performance tuning.
Bonus Skills
- Experience with LLMs, prompt engineering, or LangChain.
- Familiarity with vector databases (Pinecone, FAISS, Milvus).
- Exposure to frontend frameworks (React, Vue) for prototyping.
- Understanding of data security and compliance in regulated domains (e.g., fintech, healthcare).
