- 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 :

- 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.

Nice to have :

- 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.