Senior Machine Learning Engineer

The role covers architecture, production deployment, and continuous performance monitoring.

Responsibilities:
Architect, fine-tune, and evaluate advanced ML models (including large language models) to support modular intelligent behavior.
Build reliable end-to-end ML workflows: data ingestion, feature preparation, model training, deployment, and monitoring.
Develop and maintain scalable serving infrastructure using containers (Docker, Kubernetes) and integrate with backend services.
Establish evaluation pipelines, run A/B experiments, and track performance metrics to ensure model effectiveness.
Guarantee reproducibility, compliance, and transparency across the full ML lifecycle.
Work with backend teams to define service SLAs and optimize real-time inference.
Mentor teammates and help shape engineering best practices.

Requirements:
5+ years of experience as an ML Engineer or applied researcher with hands-on deployment of production models.
Advanced Python skills with strong knowledge of ML frameworks (PyTorch, TensorFlow) and modern LLM tooling (e.g. HuggingFace).
Proficiency in MLOps: Docker, Kubernetes, model serving frameworks (Triton, FastAPI), CI/CD automation.
Familiarity with data pipelines, SQL, and cloud ML platforms (AWS, GCP, Azure).
Strong grasp of experimentation, A/B testing, and ML performance measurement.
Collaborative mindset, comfortable working with product, data, and engineering teams.

Preferred:
Experience with conversational AI, retrieval-augmented generation, or orchestrating multi-model systems.
Knowledge of vector search technologies (FAISS, Pinecone) and modern embedding methods.
Understanding of prompt design and RLHF approaches.
Startup background and ability to thrive in fast-changing environments.

What We Offer:
Competitive salary 
Direct collaboration with seasoned founders and industry experts.
Fast-paced environment with minimal bureaucracy.
Opportunity to shape cutting-edge AI applications in a high-impact domain.