Note: The job is a remote job and is open to candidates in USA. Grid Dynamics is a technology consulting, platform and product engineering, AI, and advanced analytics services company serving enterprise organizations undergoing business transformation. The Senior Machine Learning Engineer will design, deploy, and scale personalized recommendation and ranking models while building the ML infrastructure, production systems, deployment pipelines, and monitoring capabilities that support AI products across the marketplace.

Responsibilities

• Partner with Data Scientists, Product Managers, and peer engineering teams to build and ship AI product features
• Design, build, and maintain scalable ML infrastructure and production systems, including batch and streaming data pipelines for model training and serving
• Deploy, serve, and monitor models in public cloud environments via microservices that meet throughput and latency requirements
• Define best practices for AI product development: pipeline automation, model versioning, deployment, and scalable serving
• Design and manage CI/CD pipelines for machine learning deployments
• Integrate models into search and ranking systems (e.g., Elasticsearch first-stage retrieval and downstream ranking services)
• Own accountability for the implementation of AI product features end to end
• Use modern monitoring and telemetry tooling to identify, assess, and prioritize platform issues
• Support experimentation and A/B testing infrastructure, offline evaluation harnesses, and search-quality reporting
• Stay current on emerging AI/ML technology and bring the best of it to the team

Skills

• MS or PhD in Computer Science, Engineering, Mathematics, or equivalent practical experience
• 5+ years in machine learning and backend software engineering
• Deep understanding of user modeling, embeddings, similarity search, and ranking models
• Strong experience with serving architectures (REST/gRPC APIs, model servers) and low-latency inference
• Hands-on experience with modern AI development tooling — MCP servers/clients, PydanticAI, LangGraph, LiteLLM, or similar
• Experience with ML pipeline and experiment-tracking tools (MLflow, Kubeflow, Airflow, Weights & Biases, or similar)
• Solid grasp of distributed systems, microservices, and system design
• Cloud experience (AWS, GCP, or Azure) and containerized development (Docker, Kubernetes)
• Experience with SQL and NoSQL databases and large-scale datasets
• Familiarity with the full data science workflow — data ingestion, feature engineering, experimentation, deployment, and monitoring
• Exposure to experimentation platforms and online A/B testing
• Strong written and verbal communication; able to explain ML results and applications to scientists, engineers, and business partners
• Strong proficiency in Python, plus at least one other backend language (Go or Java preferred)

Benefits

• Opportunity to work on cutting-edge projects
• Work with a highly motivated and dedicated team
• Flexible schedule
• Benefits package - medical insurance, vision, dental, etc.
• Corporate social events
• Professional development opportunities
• Well-equipped office

Company Overview

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