AI Summary
Lead the design, implementation, and scaling of ML infrastructure and production AI systems. Partner with AI researchers, engineers, and stakeholders to ensure robust, efficient, and automated AI systems. Drive strategy and hands-on execution.
Key Highlights
Architect and scale ML Ops pipeline
Design reliable infrastructure for model deployment
Optimize compute usage and implement observability
Key Responsibilities
Architect, build, and scale the end-to-end ML Ops pipeline
Design reliable infrastructure for model deployment, versioning, reproducibility, and orchestration
Optimize compute usage across distributed systems
Lead the implementation of observability for ML systems
Build automated workflows for dataset curation, labeling, feature pipelines, evaluation, and CI/CD for ML models
Technical Skills Required
Benefits & Perks
Competitive salary
Equity options
Sign-on bonus
Health insurance
Dental insurance
Vision insurance
401k
Nice to Have
Experience deploying and operating LLMs and generative models in production at enterprise scale
Familiarity with DevOps, CI/CD, automated deployment pipelines, and infrastructure-as-code
Job Description
Location: Cambridge, MA (Eastern Time / UTC -4) Relocation package available
Start date: ASAP
Languages: English (required)
About The Role
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 seeking a Staff / Principal ML Ops Engineer to lead the design, implementation, and scaling of the companys ML infrastructure and production AI systems. This is a high-impact, architecture-defining role where youll work across the entire model lifecycletraining, evaluation, deployment, observability, and continuous optimization.
You will partner closely with AI researchers, GPU systems engineers, backend teams, and product stakeholders to ensure the companys large-scale AI systems are robust, efficient, automated, and production-grade. This role is ideal for someone who has already built and owned ML platforms at scale and can drive strategy as well as hands-on execution.
What Youll Do
- Architect, build, and scale the end-to-end ML Ops pipeline, including training, fine-tuning, evaluation, rollout, and monitoring.
- Design reliable infrastructure for model deployment, versioning, reproducibility, and orchestration across cloud and on-prem GPU clusters.
- Optimize compute usage across distributed systems (Kubernetes, autoscaling, caching, GPU allocation, checkpointing workflows).
- Lead the implementation of observability for ML systems (monitor drift, performance, throughput, reliability, cost).
- Build automated workflows for dataset curation, labeling, feature pipelines, evaluation, and CI/CD for ML models.
- Collaborate with researchers to productionize models and accelerate training/inference pipelines.
- Establish ML Ops best practices, internal standards, and cross-team tooling.
- Mentor engineers and influence architectural direction across the entire AI platform.
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- Deep hands-on experience designing and operating production ML systems at scale (Staff/Principal-level expected).
- Strong background in ML Ops, distributed systems, and cloud infrastructure (AWS, GCP, or Azure).
- Proficiency with Python and familiarity with TypeScript or Go for platform integration.
- Expertise in ML frameworks: PyTorch, Transformers, vLLM, Llama-factory, Megatron-LM, CUDA / GPU acceleration (practical understanding)
- Strong experience with containerization and orchestration (Docker, Kubernetes, Helm, autoscaling).
- Deep understanding of ML lifecycle workflows: training, fine-tuning, evaluation, inference, model registries.
- Ability to lead technical strategy, collaborate cross-functionally, and operate in fast-paced environments
- Experience deploying and operating LLMs and generative models in production at enterprise scale.
- Familiarity with DevOps, CI/CD, automated deployment pipelines, and infrastructure-as-code.
- Experience optimizing GPU clusters, scheduling, and distributed training frameworks.
- Prior startup experience or comfort operating with ambiguity and high ownership.
- Experience working with data engineering, feature pipelines, or real-time ML systems.
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- Research pedigree: MIT CSAIL founders recognized for breakthrough AI and systems contributions.
- Customer impact: Deploy AI solutions powering Fortune 500 clients.
- Industry momentum: Lab alumni have led high-value acquisitions (MosaicML Databricks, Run:AI Nvidia, W&B CoreWeave).
- Funding & growth: Oversubscribed seed round, next funding in 2026.
- Career growth & influence: Lead AI initiatives, optimize pipelines, and directly impact production AI systems at scale.
- Culture & autonomy: Own critical systems while collaborating with world-class engineers.
- Aspirational impact: Solve AI performance challenges few engineers ever face.
- Competitive salary & equity options
- Sign-on bonus
- Health, Dental, and Vision
- 401k
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