We are seeking a Senior Machine Learning Engineer to design, build, and deploy scalable ML and LLM-powered systems in production.

This role emphasizes engineering excellence over pure research, with a strong focus on cloud-native architectures, model reliability, and real-world performance.

You will work on advanced ML systems, including agentic and LLM-based workflows, that support NetSPI's security platforms.

Responsibilities :

- Design, build, and deploy production-grade machine learning and LLM-drive systems

- Train, validate, fine-tune, and evaluate ML and large language models using sound statistical practices

- Develop end-to-end ML pipelines covering data ingestion, preprocessing, deployment, monitoring, and retraining

- Build and optimize agentic systems, RAG pipelines, and LLM orchestration workflows

- Optimize models for inference latency, scalability, and cost efficiency in AWS environments

- Collaborate with product, security, and engineering teams to integrate ML into core platform capabilities

- Uphold high engineering standards through clean code, testing, documentation, and code reviews

- Contribute to the evolution of ML platform reliability, observability, and developer experience

Our General Tech Stack :

- Languages : Python (primary), Java, Go

- ML & AI : PyTorch, TensorFlow, scikit-learn, LLM fine-tuning frameworks

- Infrastructure : AWS (ECS, Fargate, Lambda, S3, RDS Aurora, SageMaker)

- Data & Messaging : PostgreSQL, Redis, Kafka

- MLOps & Platform : Docker, Terraform, GitHub Actions, CI/CD, model monitoring

- APIs & Services : FastAPI, Flask, gRPC

- Orchestration : Temporal workflows

Requirements :

- Fluency in English (written and spoken)

- 5+ years of professional ML experience, including production deployments

- Strong background in machine learning, statistics, and model evaluation

- Demonstrated experience training, validating, and fine-tuning LLMs

- Hands-on experience with agentic systems and modern NLP architectures

- Strong Python engineering skills and experience with ML frameworks

- Proven experience building ML systems on AWS-based cloud architectures

- Solid software engineering fundamentals (data structures, algorithms, system design)

- Proficiency in version control (Git), testing frameworks (pytest/unittest), and CI/CD pipelines

- Familiarity with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes).

- Minimum Bachelor's degree in a relevant technical field

Preferred :

- Graduate degree in a relevant field such as Computer Science, Data Science, or Applied Mathematics

- Exposure to MLOps or ML infrastructure tooling (e.

- MLflow, Kubeflow, Airflow, BentoML, SageMaker).

- Familiarity with feature stores, data versioning, and model monitoring tools.

- Experience optimizing models for edge or low-latency deployment environments.

- Contributions to open-source ML or data tooling projects.

What We Value :

- Pragmatism and an action-oriented mindset, favoring delivery and measurable results over theoretical perfection.

- Low-ego, highly collaborative mindset with a strong bias toward shared ownership and team success over individual credit.

- Strong architectural and algorithmic thinking applied to real-world systems.

- Commitment to writing production-grade, performant, and maintainable code