Senior AI/ML Engineer

Pragmatike•Cambridge, MA

17h•Onsite

About The Position

Pragmatike is hiring on behalf of a fast-growing AI startup recognized as a Top 10 GenAI company by GTM Capital, founded by MIT CSAIL researchers. We are looking for a Senior AI/ML Engineer to join a high-performing AI engineering team building and deploying real-world AI systems used by Fortune 500 customers. You will take technical ownership of critical ML components, lead model development and productionization efforts, and work closely with AI researchers, platform engineers, and product teams to deliver scalable, production-grade AI systems. This role is intended for experienced AI/ML engineers who have already built and deployed models in production environments and want to operate at the intersection of research-quality AI and real-world systems engineering.

Requirements

Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Mathematics, or a related field
7+ years of professional experience in AI/ML, data science, or applied machine learning roles
Proven experience deploying ML systems into production environments
Strong understanding of ML fundamentals, evaluation methodologies, and model lifecycle management
Deep hands-on experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn
Strong proficiency in Python
Experience designing scalable ML systems and pipelines
Ability to operate independently and take technical ownership of complex systems
Strong problem-solving, architecture, and system-design skills

Nice To Haves

Experience with deep learning, LLMs, GenAI, or modern AI architectures
Experience building AI systems used by enterprise or large-scale customers
Strong experience with cloud platforms (AWS, GCP, Azure)
MLOps experience (CI/CD for ML, model deployment, monitoring, observability)
Research background, publications, or applied AI innovation projects
Experience leading AI initiatives or mentoring ML engineers

Responsibilities

Design, build, train, and optimize machine learning and deep learning models for production use
Lead the deployment of ML models into scalable, reliable production systems
Architect training, inference, and evaluation pipelines for structured and unstructured data
Own model performance, reliability, scalability, and lifecycle management
Drive experimentation, model iteration, and performance benchmarking
Implement and maintain high-quality ML codebases primarily in Python
Monitor, debug, and improve live production models
Collaborate with platform, backend, and infrastructure teams on system integration
Contribute to technical direction, architecture decisions, and AI roadmap planning
Mentor junior engineers and support team-level technical excellence