NLP Machine Learning Engineer
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Job Location:
Monthly Salary:
Posted on:
7 hours ago
Vacancies:
1 Vacancy
Job Summary
Role Summary:
We are seeking a Machine Learning Engineer with experience in Natural Language Processing (NLP) who is deeply focused on
building production-grade systems. This is not a research role - were looking for someone who can independently translate ML
prototypes into scalable maintainable systems and understands how to integrate ML into real-world products with all their
engineering complexity.
This role is ideal for someone who enjoys working at the intersection of ML software engineering and infrastructure - and is
motivated by seeing their models deployed and performing reliably in production.
Responsibilities:
Build and maintain robust scalable NLP-based systems for real-world use cases
Design and implement end-to-end ML pipelines - from data preprocessing and model inference to deployment and
monitoring
Work with FastAPI/Flask to expose models as services via REST APIs
Deploy models using platforms like AWS SageMaker Bedrock or Triton Inference Server
Containerize and manage deployments using Docker and Kubernetes
Contribute to the design of system architecture and data flows across services
Collaborate with backend infra and data engineering teams to ensure reliable model integration
Drive MLOps best practices including model versioning automated testing CI/CD and logging
Optimize models and pipelines for latency scalability and cost-efficiency
Stay up-to-date with NLP techniques but apply them with a product-first mindset
Requirements:
2 to 5 years of experience in ML Engineering with a strong NLP foundation
Proven experience building and deploying NLP systems in production
Strong Python programming skills and experience with modern Python service frameworks (FastAPI/Flask)
Hands-on experience with PyTorch or TensorFlow
Solid understanding of text processing embeddings feature extraction and semantic similarity
Experience deploying on AWS (SageMaker Lambda S3 EC2) or similar cloud platforms
Strong grasp of system-level design: designing APIs fault tolerance observability performance
Comfortable with Docker and Kubernetes for containerized deployment
Familiarity with CI/CD pipelines monitoring tools (e.g. Prometheus Grafana) and model versioning (e.g. MLflow)
Experience handling unstructured text data and designing feature pipelines for classification or extraction tasks
Independent self-driven mindset with a bias for action and a strong sense of ownership
2 to 5 years of experience in ML Engineering with a strong NLP foundation
Proven experience building and deploying NLP systems in production
Strong Python programming skills and experience with modern Python service frameworks (FastAPI/Flask)
Hands-on experience with PyTorch or TensorFlow
Solid understanding of text processing embeddings feature extraction and semantic similarity
Experience deploying on AWS (SageMaker Lambda S3 EC2) or similar cloud platforms
Strong grasp of system-level design: designing APIs fault tolerance observability performance
Comfortable with Docker and Kubernetes for containerized deployment
Familiarity with CI/CD pipelines monitoring tools (e.g. Prometheus Grafana) and model versioning (e.g. MLflow)
Experience handling unstructured text data and designing feature pipelines for classification or extraction tasks
Independent self-driven mindset with a bias for action and a strong sense of ownership
Bonus Points For:
Experience building or integrating with graph databases or using knowledge graphs for NLP
Experience designing or maintaining real-time data or inference systems
Familiarity with model optimization techniques such as quantization or distillation
Exposure to retrieval-augmented generation (RAG) transformers or large language models in production
Experience building or integrating with graph databases or using knowledge graphs for NLP
Experience designing or maintaining real-time data or inference systems
Familiarity with model optimization techniques such as quantization or distillation
Exposure to retrieval-augmented generation (RAG) transformers or large language models in production
Why This Role Matters:
This role ensures that NLP-based intelligence doesnt stop at research or experimentation. The candidate will play a key part in
converting ideas into stable high-impact systems bringing ML value directly to production pipelines and customer-facing
applications.
This role ensures that NLP-based intelligence doesnt stop at research or experimentation. The candidate will play a key part in
converting ideas into stable high-impact systems bringing ML value directly to production pipelines and customer-facing
applications.
Key Skills
- Industrial Maintenance
- Machining
- Mechanical Knowledge
- CNC
- Precision Measuring Instruments
- Schematics
- Maintenance
- Hydraulics
- Plastics Injection Molding
- Programmable Logic Controllers
- Manufacturing
- Troubleshooting
