
About this role
The candidate will design, build, and maintain scalable Python codebases, APIs, and microservices. They will be responsible for the integration, optimization, deployment, and monitoring of machine learning models in live production environments using automated CI/CD and containerized infrastructure. The role involves end-to-end ownership of projects from architectural discussions to cloud infrastructure setup. The developer will work in a a fast-paced environment, partnering with product managers and cross-functional teams. The technical environment includes Python, PyTorch, TensorFlow, scikit-learn, Pandas, NumPy, Docker, Kubernetes, and cloud platforms like AWS, GCP, or Azure.
Skills & technologies
Must have
- Python
- PyTorch
- TensorFlow
- scikit-learn
- Pandas
- NumPy
- Docker
- Kubernetes
- CI/CD
- AWS
- GCP
- Azure
Read full description
If you thrive in a fast-paced, dynamic environment, love solving complex architecture problems with minimal hand-holding, and pride yourself on picking up new technologies at lightning speed, this role is for you.
Responsibilities
- Software Development: Design, build, and maintain clean, efficient, and scalable Python codebases, APIs, and microservices.
- ML Production & MLOps: Integrate, optimize, deploy, and monitor machine learning models in live production environments using automated CI/CD and containerized infrastructure.
- System Ownership: Take full end-to-end ownership of projects—from initial architectural discussions through cloud infrastructure setup, testing, and deployment.
- Rapid Adaptation: Quickly learn and adopt new tools, libraries, and domain frameworks as project priorities evolve.
- Collaboration: Partner with product managers and cross-functional teams to translate complex business needs into resilient technical solutions.
- Core Python Expertise: 3+ years of professional experience developing software in Python (clean architecture, async code, testing frameworks, and API development).
- Machine Learning Background: Hands-on experience with ML frameworks and libraries (e.g., PyTorch, TensorFlow, scikit-learn, Pandas, NumPy) and model lifecycle management.
- MLOps & Infrastructure: Proven experience deploying and maintaining ML models in production using Docker, Kubernetes, CI/CD pipelines, and cloud platforms (AWS, GCP, or Azure).
- Independent & Self-Motivated: Proven track record of taking initiative, driving projects forward with minimal supervision, and figuring out ambiguous problems on your own.
- Quick Learner: Demonstrated ability to rapidly absorb new concepts, technologies, and codebases.
- Fast-Paced Adaptability: Ability to prioritize effectively, context-switch when necessary, and ship reliable code in a fast-moving environment.
- Availability to join evening calls (till 21:00)
- English level - Upper Intermediate or higher
- Java Knowledge: Familiarity with Java (e.g., Spring Boot, enterprise integrations, working within legacy systems) is a strong plus.
- Data Engineering: Exposure to SQL/NoSQL databases and data streaming tools (Kafka, Spark, Airflow).
- 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
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.