Machine Learning Engineer
- Posted a month ago
- Be among the first 10 applicants
Job Description
Responsive is looking for an ML Engineer with a strong background in Python, NLP, structured and unstructured data, and basic understanding of ML and DL algorithms/frameworks. The ideal candidate will have a Bachelor's degree in any quantitative discipline, such as Engineering, Computer Science, IT, or Statistics. The ML Engineer should be familiar with Linux and Git and have strong problem-solving and analytical skills. Additionally, the candidate should have a good understanding of mathematics.
Essential Responsibilities
- To develop and implement ML models and algorithms that improve the company's products and services.
- To Work with large datasets to analyze, model, and interpret data.
- To create and optimize NLP models to extract meaningful insights from unstructured text data.
- To collaborate with cross-functional teams to identify and solve complex business problems.
- To continuously monitor and improve the performance of ML models.
- To develop and maintain ML codebase, including version control using Git.
Education
Bachelor's degree in any quantitative discipline, such as Engineering, Computer Science, IT, or Statistics.
Experience
- 4-6 years of experience in Machine Learning
- Proficiency in Python is a must
Knowledge, Ability & Skills
- Comfortable with NLP techniques.
- Basic understanding of ML and DL algorithms/frameworks.
- Familiarity with Linux and Git.
- Good problem-solving and analytical skills.
- Good understanding of mathematics.
More Info
About Company
Skills:
Machine Learning, Deep Learning, Tensorflow, Pytorch, Opencv, Python, Computer Vision, GenAI, Prompting, LLMs, GenAI systems, OCR engines, PIL, Fine-tuning
Skills:
Tensorflow, Pytorch, Python, MLops, Spark, Databricks, GPU infrastructure, model versioning, event tokenization, efficient attention, attention-based architectures, positional embeddings, mixed-precision training, self-supervised pretraining, gradient accumulation, automated retraining, experiment tracking, masked modeling, deep learning models, distributed strategies, contrastive learning, check-pointing, downstream fine-tuning, Production Monitoring
Skills:
Jax, Tensorflow, Nlp, Pytorch, Python, ASR, Agentic RAG pipelines, ML lifecycle evaluation and deployment, Llm, TTS, state space models, self-learning agents, LLM orchestration, low-latency real-time ML applications
Skills:
Java, Docker, Kubernetes, Python, TFX, AWS SageMaker, Kubeflow, Google Vertex AI, Go, MLflow, Metaflow
Skills:
data engineering , Numpy, Pandas, Pytorch, Python, Testing and release, scikit-learn, Development processes, ELT data pipelines, ML research methodologies, Build processes, Production ML systems, Source control management, ML pipelines