AI/ML Engineer

As an AI/ML Engineer, you will be a key contributor to building and enhancing AI/ML platforms that drive digital transformation and operational efficiency. You will focus on the development and implementation of innovative generative AI solutions—such as LLMs and RAG systems—to solve complex business challenges. Your work will involve optimizing cloud infrastructure (GCP), building autonomous AI Agents, and ensuring the continuous improvement of AI-powered systems through hands-on development and integration.

Responsibilities

Key Responsibilities

• Model Development & Deployment: Build, deploy, and maintain AI/ML models, with a heavy focus on Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) architectures.
• AI Agent Orchestration: Design and optimize AI Agents for specialized functions like engineering onboarding and HR assistance, focusing on training systems for continuous learning.
• Workflow Automation: Design and build components for step and flow automation, enabling AI assistants to initiate and execute workflows across multiple enterprise systems (e.g., creating tickets, scheduling meetings, provisioning accounts).
• Data Engineering: Implement robust data ingestion, chunking, and embedding creation processes for both structured and unstructured data.
• Model Optimization: Contribute to the continuous improvement of AI models through prompt engineering, versioning, tracking, and analysis of chat dialogs.
• Cloud & FinOps Integration: Work within GCP to integrate AI/ML solutions and develop components for FinOps and cloud cost optimization.
• Cross-Functional Collaboration: Partner with Architects, Data Scientists, and other engineers to translate design specifications into robust and scalable AI/ML solutions.
• Troubleshooting: Identify and resolve issues related to AI/ML model performance, data pipelines, and system integrations.

Requirements

• Total Professional Experience: 5–7 Years
• Core AI/ML Expertise: Proven experience developing and deploying AI/ML models, specifically LLM and RAG-based architectures.
• Technical Proficiency: Strong programming skills in Python and hands-on experience with Vector Databases.
• Cloud & Infrastructure: Experience with GCP (preferred), containerization (Docker), and orchestration (Kubernetes).
• Data & Integration: Experience with data processing tools (e.g., Spark), a strong understanding of APIs, and experience with system integrations.
• Software Engineering: Solid knowledge of SDLC best practices, methodologies, and version control systems (e.g., GitHub).
• Preferred (Nice to Have): * Experience in FinOps and cloud cost optimization initiatives.
• Familiarity with incident response tools (e.g., PagerDuty, Opsgenie) or conversational AI frameworks.
• Understanding of data governance, security, and compliance in AI/ML systems.
  

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.