PYTHON AGENTIC AI ENGINEER

Roles and Responsibilities

• Design and develop autonomous and multi-agent solutions using Google ADK or Agent SDK.
• Build agent workflows, tool calling, orchestration, and context management capabilities.
• Integrate Gemini/OpenAI models using prompt engineering, function calling, structured outputs, grounding, and evaluation techniques.
• Develop RAG solutions using embeddings and vector databases.
• Develop and integrate REST APIs, microservices, and event-driven architectures.
• Develop scalable backend applications using Python, Django, GraphQL, and PostgreSQL.
• Deploy and manage cloud-native applications on AWS.
• Support data pipelines/ETL, application security, scalability, and resilience.
• Implement AI observability, evaluation, security, governance, and guardrails.
• Apply Domain-Driven Design (DDD), agile methodologies, testing, and code-review best practices.
• Collaborate with product, engineering, data science, design, and business teams.

Required Skills & Experience

• 7+ years of professional software engineering/system architecture experience.
• Strong expertise in Python, particularly Django and GraphQL.
• Hands-on experience with AI agent development using Google ADK or Agent SDK.
• Strong experience with LLM integration, prompt engineering, and agent orchestration.
• Experience with Gemini/OpenAI models, tool calling, function calling, structured outputs, and grounding.
• Hands-on experience building RAG applications, vector databases, and embeddings.
• Strong experience with AWS cloud-native application development.
• Experience with REST APIs, microservices, SOA, and event-driven architectures.
• Experience with PostgreSQL and data pipelines/ETL.
• Knowledge of AI observability, evaluation, security, governance, and guardrails.
• Strong understanding of scalable, resilient, and production-ready system architecture.
• Strong collaboration and communication skills.

Preferred Skills

• React and Apollo GraphQL.
• Domain-Driven Design (DDD).
• Agile methodologies.
• AI evaluation frameworks.
• CI/CD and automated testing.
• Experience working in collaborative, in-person engineering environments.