The best people deserve the best rewards.

In addition to the benefits youd expect from a global leader (health insurance, paid time off, etc.) we also offer :

- Competitive compensation.

- Career growth opportunities.

- Flexibility and Support for Diverse Life Stages and Choices.

- Wellbeing programs including Physical & Mental wellness.

Required Experience :

- 5 to 8 years of experience in software engineering, with at least 2 years focused on AI/ML or LLM-based systems in production environments.

- Experience designing and deploying AI-powered applications in cloud-native Azure environments.

- Demonstrated experience building RAG pipelines and working with vector databases.

- Experience developing distributed, API-driven systems and integrating with enterprise platforms.

- Experience working within Agile product teams delivering iterative releases.

- Excellent communication skills and a desire to collaborate openly within a fast-moving team.

- Interpersonal Skills leadership, interactions, communication, influence.

Other Skills :

- Hands on experience with LangChain, LangGraph (Agents, Tools) and agent orchestration patterns.

- Practical implementation experience with MCP and A2A communication models.

- Experience with Azure OpenAI, Gemini and model integration strategies.

- Strong knowledge of Retrieval-Augmented Generation (RAG) architectures.

- Experience with vector stores such as Azure AI Search and PGVector.

- Proficiency in prompt engineering, evaluation strategies, and structured output validation.

- Experience developing RESTful APIs and integrating external services.

- Experience with Docker and containerized deployment models.

- Experience implementing CI/CD pipelines using GitHub Actions or similar platforms.

- Familiarity with feature flag strategies, telemetry, logging, and monitoring.

- Experience working with NoSQL databases and distributed system patterns.

Preferred Experience :

- Experience designing multi-agent orchestration strategies across distributed systems.

- Exposure to performance and cost optimization strategies for LLM-based systems.

- Experience contributing to shared AI platform components or reusable internal frameworks.

- Familiarity with enterprise governance, security, and responsible AI considerations.