Senior Python Engineer

Grid Dynamics; United States; On-site; USD 150,000 - 190,000; Full time

Benefits offered by this job

Competitive salary

Flexible schedule

Medical insurance

Vision & dental

Job summary

Grid Dynamics is seeking a skilled MLOps/DevOps engineer to design and deploy ML infrastructure on Cloud AI Platform. You will partner with product teams to understand use cases, build production-ready services, and translate requirements into scalable platform capabilities.

Responsibilities include building pipelines, improving APIs, and mentoring teams. Proficiency with Docker, Kubernetes, IaC (Terraform, CloudFormation), CI/CD (Jenkins, GitHub Actions), Airflow/Dagster, Kafka/Kinesis is

Qualifications

• Experience designing ML infrastructure and deployment pipelines.

Responsibilities

• Partner with internal product teams to understand AI/ML use cases and translate requirements into technical solutions.
• Build production-ready services, integrations, workflows, and developer tooling on top of Cloud AI Platform.
• Prototype solutions rapidly, validate approaches with customers, and harden successful prototypes for production.
• Identify recurring customer needs and translate them into reusable platform capabilities and tooling.
• Collaborate with platform teams to improve APIs, SDKs, workflows, documentation, and developer experience.

Skills

MLOps

Cross-functional communication

Applied ML awareness

DevOps practices

Platform thinking

Education

BS in Computer Science

MS in Computer Science / Software Engineering / ML

Tools

Docker

Kubernetes

Terraform

CloudFormation

Jenkins

GitHub Actions

Airflow

Dagster

Kafka

Kinesis

Prometheus

Grafana

ELK stack

Job description

Our team builds the developer tooling, platforms, systems and experiences that power Cloud AI Platform. In this role you will partner directly with internal customers to understand their use cases, evaluate technical requirements, and build AI-driven systems and solutions that leverage Cloud AI Platform capabilities. You will prototype quickly, harden solutions for production, build services and feed insights back to platform teams to influence roadmap and improve the developer experience.

You will also act as a bridge between product management, partner platform, and customer teams by helping define best practices, documenting patterns, and working closely with platform engineering groups to drive alignment and deliver systems. Success in this role requires a combination of strong engineering fundamentals, applied ML awareness, platform thinking, customer empathy, and the ability to deliver in fast-evolving environments.

Responsibilities

• Partner directly with internal product teams to understand AI/ML use cases and translate requirements into technical solutions.
• Build production-ready services, integrations, workflows, and developer tooling on top of Cloud AI Platform.
• Prototype solutions rapidly, validate approaches with customers, and harden successful prototypes for production.
• Identify recurring customer needs and translate them into reusable platform capabilities and tooling.
• Collaborate with platform teams to improve APIs, SDKs, workflows, documentation, and developer experience.

Requirements

• Experience designing, building, and maintaining ML infrastructure and deployment pipelines using containerization technologies (Docker, Kubernetes preferred) and cloud platforms (AWS, Azure, or GCP)
• Proficient coding skills in Python, Go, or Scala
• Excellent grasp of software engineering fundamentals and DevOps practices
• Strong experience with Infrastructure as Code (Terraform, CloudFormation) and CI/CD tools (Jenkins, GitLab CI, GitHub Actions)
• Experience with data pipeline orchestration tools (Airflow, Prefect, Dagster) and streaming platforms (Kafka, Kinesis)
• Proficient knowledge of Git and collaborative development workflows
• Proficiency in monitoring and observability tools (Prometheus, Grafana, ELK stack) for ML model performance and system health
• BS, MS in Computer Science, Software Engineering, Machine Learning, or equivalent degree with applicable experience
• 3+ years of experience in MLOps, DevOps, or related infrastructure roles
• Experience working in cross-functional teams and communicating technical concepts to diverse audiences

Nice to have

• Experience in ML frameworks (TensorFlow, PyTorch, MLflow, Kubeflow)
• Understanding of security best practices for ML systems and data governance
• Knowledge of ML model versioning, experiment tracking, and feature stores (MLflow, Weights & Biases, Feast)
• Experience with automated testing frameworks for ML systems, including data validation and model testing

All onboarding are conducted in person and the candidate will be required to visit one of our offices on their first day of employment.

We offer

• Opportunity to work on cutting-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, vision, dental, etc.
• 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.