Machine Learning Engineer
Responsible for developing, training, optimizing, and deploying machine learning models for scalable AI/ML applications. The role involves improving model performance, building efficient ML pipelines, and automating processes across the ML lifecycle.
Responsibilities:
- Understanding product requirements and converting them into engineering solutions.
- Designing Database schema to optimize performance.
- Interacting with clients for requirement gathering, stand-ups, and progress meetings Documenting the machine learning process.
- Design / Review application and system architecture.
- Write code yourself and do peer code reviews to ensure consistency in code style.
- Do the necessary research, data analysis, and train and deploy models.
- Build efficient data pipelines.
- Able to understand and provide metrics to evaluate according to the business requirement.
Skills:
- Pandas, NumPy and Sci-Kit Learn
- Docker, ElasticSearch, Google Cloud,AWS Cloud Services
- TF, Keras, Pytorch
- MongoDB, Postgres, MySQL
- Flask, Django or Fast API
- Matplotlib, Ggplot, or Seaborn
- TensorBoard
- LangChain
- LlamaIndex
- HuggingFace
- Onnx graph optimization
- Transformers
Requirements:
- The preferred experience is 2 years and above with a relevant degree.
- Well-versed in any one of MLOps tools.
- Well versed in system design & architecture design principles
- Understanding of the basic Machine Learning concepts.
- Experience with Python and its scientific stack.
- Good familiarity with computer science concepts such as OOP, Data Structures, Algorithm Design, and Optimization of Code.
- Must have Knowledge of either CV or NLP.
- Work in Anomaly detection and Recommendation systems is a plus point
- Keeping up to date with the latest ML research papers and technologies.
Who You’ll Work With
You’ll work with ambitious, supportive teammates focused on learning, growing, and building meaningful solutions.
What You’ll Do
Lead initiatives, solve meaningful problems, and build impactful solutions.
