:

You'll make an impact by :

- Agent harness architecture - design and own the runtime that wraps LLMs: orchestration, tool integration, memory, security
- Determinism boundary - enforce separation between AI-assisted interpretation and deterministic computation
- Multi-posture deployment - ensure the system runs on-premises, hybrid, and cloud with identical trust guarantees
- Knowledge engineering - design how domain knowledge is captured, versioned, and consumed by both AI and deterministic components
- Traceability and governance - audit trails, human-in-the-loop approval gates, structured error handling
- Portfolio scaling - generalize patterns across agent use cases; maintain architecture documentation.

Requirements :

- Education: Bachelors or masters equivalent.
- 7 years software architecture, 3 years involving AI/ML systems in production
- Experience separating deterministic and probabilistic concerns in a single system
- Multi-setting deployment where data sovereignty or offline operation were real constraints
- Work in a regulated or safety-adjacent domain (energy, medical, automotive, aerospace, industrial)
- Generative AI Agentic orchestration frameworks, RAG, designing LLMs as orchestrators rather than decisionmakers
- Safety-critical design:



Trust boundaries between probabilistic and deterministic components, audit and provenance architecture
- Knowledge engineering: Structured and versioned domain knowledge, knowledge graphs or semantic technologies
- Deployment: multi-posture delivery (on-prem cloud), local and cloud-hosted LLM inference, CI/CD
- Security & identity: Auth2/OIDC, zero-trust principles, least-privilege access for AI tool integration
- AI evaluation: prompt/model versioning, regression testing, evaluation pipelines, model promotion governance
- Integration design: API design across heterogeneous systems, complex domain data format parsing and transformation
- Observability: Monitoring, tracing, and logging for production AI systems
- Architecture practice: structured documentation, decision records, hypothesis-driven design

Desired Domain Exposure :

- Electrical power systems or protection engineering
- Industrial automation, OT/IT convergence
- Energy sector product development

Technology Landscape :

- Python, TypeScript, LLM orchestration frameworks, Knowledge graphs, Azure cloud, Local LLM inference, OAuth2/OIDC, IEC 61850, COMTRADE.

📌 AI Software Architect (India)
🏢 Le Human Resources Solutions
📍 India