Lead MLOps Engineer – Privacy-First AI/ML Infrastructure
We're looking for a Lead Machine Learning Engineer to join our Engineering team, focused on designing and scaling privacy-first AI/ML infrastructure for global products. In this role, you'll work at the intersection of large-scale distributed systems and privacy engineering, building the platforms and tools that power ML models across our products - while ensuring user privacy is treated as a core engineering requirement, not an afterthought.
Essential functions
As a Machine Learning Engineer on the project, Engineer will dive into a variety of unique problems:
- Build & Innovate: Research and develop state-of-the-art AI/ML solutions and tooling that transform how we train, deploy, and monitor models powering our products.
- Scale & Secure: Design secure, private, and highly performant systems - ensuring privacy protection is built into our infrastructure by design, not bolted on.
- Collaborate & Influence: Act as a technical leader - partnering with cross-functional teams, contributing to design discussions, exchanging constructive feedback, and mentoring junior engineers.
- Champion Quality: Drive engineering excellence through design reviews, rigorous code reviews, and robust test automation, ensuring our AI/ML systems remain maintainable and resilient at scale.
Qualifications
Key Qualifications
- Experience: 5-7+ years of professional software engineering experience with a heavy focus on AI/ML.
- Education: Master's or PhD in Machine Learning, Computer Science, Computer Engineering, or equivalent experience.
- Core Expertise: Deep understanding of traditional ML (supervised/unsupervised) and Generative AI, strong system design skills, and experience with high-scale distributed data processing.
- Strong programming skills in Python, with working proficiency in Java and/or Scala
- Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or JAX
- Proven experience with distributed computing frameworks - Ray, Apache Spark
- Solid expertise in Kubernetes and containerized infrastructure for ML workloads
- Experience building and maintaining ML pipelines with tools like MLflow
- Demonstrated experience designing and scaling production ML infrastructure
- Experience mentoring engineers and acting as a technical leader within a team
Would be a plus
- Experience with privacy-preserving ML techniques (e.g., differential privacy, federated learning, secure multi-party computation)
- Familiarity with Generative AI / LLM developer tooling
- Experience working on global-scale products with high traffic/data volume
- Background in security engineering or privacy-focused system design
- Contributions to open-source ML infrastructure projects
We offer
- Opportunity to work on bleeding-edge projects
- Work with a highly motivated and dedicated team
- Competitive salary
- Flexible schedule
- Benefits package - medical insurance, sports
- Corporate social events
- Professional development opportunities
- Well-equipped office
About us
Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.
