- Core Backend Development: Design, build, and maintain scalable backend services and APIs using Python (FastAPI/Django/Flask).
- Agentic AI Implementation: Architect and develop autonomous AI agents capable of multi-step reasoning, tool use, and complex decision-making to automate business workflows.
- GenAI & RAG Integration: Build and optimize Retrieval-Augmented Generation (RAG) pipelines to ground LLMs in proprietary data, ensuring high accuracy and low latency.
- Production Deployment: Take ownership of the full software lifecyclefrom architectural design to deployment and maintenance in a production environment. Monitor system health, performance, and reliability.
- Tooling & Orchestration: Integrate Generative AI tools (e.g., OpenAI, Anthropic, LangChain, LlamaIndex) into existing application logic.
- Collaboration: Work closely with frontend engineers, data scientists, and product managers to translate complex AI capabilities into user-friendly features.
Responsibilities
- Strong Python Proficiency: Deep understanding of Python internals, asynchronous programming, and modern web frameworks.
- Production Experience: Proven track record of deploying and maintaining at least one application in a production environment. You know what it takes to keep a system running (logging, monitoring, error handling).
- GenAI & Agentic Mindset: Hands-on experience with Generative AI tools and a conceptual understanding of how to build systems where AI models act as agents rather than just text generators.
- Database Skills: Proficiency with SQL (PostgreSQL, MySQL) and experience with vector databases (Pinecone, Weaviate, Milvus, or pgvector).
Requirements
- Core Backend Development: Design, build, and maintain scalable backend services and APIs using Python (FastAPI/Django/Flask).
- Database Skills: Proficiency with SQL (PostgreSQL, MySQL) and experience with vector databases (Pinecone, Weaviate, Milvus, or pgvector). GenAI & RAG Integration: Build and optimize Retrieval-Augmented Generation (RAG) pipelines to ground LLMs in proprietary data, ensuring high accuracy and low latency.
Nice to have
- Hands-on RAG & Agent Build: Demonstrated experience specifically building RAG architectures or functional AI Agents that interact with external APIs/tools.
- Data Science/ML Background: Familiarity with machine learning concepts, model evaluation, or data engineering pipelines is a strong plus.
- Cloud Native: Experience with Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure).
- Prompt Engineering: Advanced skills in prompt optimization and steering LLM behavior.
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.
