Senior AI Engineer

Special Projects Group (SPG) is seeking an experienced AI/ML Data Scientist Contractor to design, build, and deploy advanced machine learning, predictive analytics, and AI-driven decision-support solutions.
This role will partner closely with business stakeholders, software engineers, product owners, and data teams to transform complex enterprise datasets into scalable forecasting, prediction, optimization, and intelligent automation capabilities.
The successful candidate will combine strong quantitative data science skills with practical experience delivering AI solutions into production environments. They will work across the full lifecycle of AI delivery, including problem definition, data engineering, model development, evaluation, deployment, monitoring, and continuous improvement.
This is a hands-on role requiring both technical depth and strong stakeholder engagement capabilities.
Responsibilities
Predictive Analytics & Forecasting
Design, develop, and deploy predictive models to support business planning, forecasting, scenario modelling, and performance optimization.
Develop machine learning solutions using structured, semi-structured, and unstructured enterprise datasets.
Create statistical models that improve forecasting accuracy and support strategic decision making.
Build explainable models and communicate key drivers, assumptions, risks, and confidence intervals.
Machine Learning Engineering
Develop production-grade ML pipelines using Python and enterprise cloud platforms.
Build feature engineering frameworks and reusable model components.
Develop automated retraining, model validation, monitoring, and performance reporting capabilities.
Partner with software engineering teams to operationalize models through APIs and scalable services.
Generative AI & Agentic Systems
Design AI-powered solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), semantic search, and agentic workflows.
Build intelligent assistants capable of reasoning over enterprise datasets and business processes.
Develop prompt engineering strategies, evaluation frameworks, and model governance approaches.
Leverage modern AI frameworks including LangChain, LangGraph, LangSmith, AutoGen, MCP/A2A-based architectures, or equivalent technologies.
Requirements
Good to Have
Experience developing reusable application instrumentation or observability libraries.
Experience with event-driven architectures and messaging platforms such as Kafka.
Exposure to performance tuning, capacity planning, and reliability engineering practices.
Experience participating in production support, incident management, and post-incident reviews.
Familiarity with Infrastructure as Code and automated environment provisioning.
Hands-on experience with Generative AI development tools such as GitHub Copilot, Cline, Claude Code, Cursor, or equivalent tools.
Exposure to RAG, LLM-powered applications, or agentic AI systems.
Soft Skills
Strong analytical, problem-solving, and debugging skills.
Ability to work independently and take ownership.
Clear communication and effective cross-functional collaboration.
Strong focus on application reliability, maintainability, and operational excellence.
Curiosity and willingness to learn evolving backend, cloud, and observability technologies.
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.

Work arrangement
Hybrid