01Overview
What We Bring To The Table :
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. .
