ā¢ Architect enterprise-grade GenAI systems using modular LLM APIs, agent orchestration frameworks, and embedding pipelines
ā¢ Design and implement autonomous agent workflows with context management, multi-agent coordination, and task delegation
ā¢ Optimize performance, latency, and accuracy through experimentation with prompt strategies, retrieval layers, and caching logic
ā¢ Lead solution reviews, enforce prompt safety and governance, and ensure alignment with security protocols
ā¢ Collaborate with platform, product, and engineering leads to define reusable patterns and scalable AI capabilities
ā¢ Guide engineering pods on GenAI design principles, system reliability, and prompt lifecycle management
ā¢ Build and maintain reusability assets ā SDKs, templates, shared agent logic ā to accelerate delivery velocity across teams
ā¢ Stay up to date with advancements in LLM tooling, orchestration abstractions, and prompt optimization techniques
Required Qualifications:
ā¢ 6ā8+ years of experience in AI/ML engineering, with a strong focus on designing and scaling GenAI applications
ā¢ Deep proficiency in Python 3.11+ and experience with LLM APIs, vector databases, embedding generation, and agent coordination
ā¢ Hands-on expertise in architecting agent-based workflows using framework-agnostic orchestration patterns
ā¢ Proven track record in deploying secure, cost-effective, cloud-native GenAI solutions (preferably in Azure ecosystem)
ā¢ Solid grasp of CI/CD, containerization, and model monitoring practices
Preferred Qualifications:
ā¢ Exposure to model context protocols (MCP) and autonomous agent-to-agent (A2A) interactions
ā¢ Contributor to reusable GenAI accelerators, prompt chaining templates, or internal developer tools
ā¢ Familiarity with governance and observability tools for LLM workflows (e.g., cost tracking, safety controls, token usage analytics)
ā¢ Ability to simplify and communicate technical decisions to both engineers and non-technical stakeholders
ecolab