AI Solutions Architect

• Design and deploy production-grade AI systems across GenAI, predictive AI, and automation
• Own the full lifecycle from architecture through deployment and performance optimization
• Serve as the technical authority for AI solution delivery across the organization

Lead GenAI, LLM, and RAG Development

• Build LLM-powered applications, assistants, and multi-agent workflows
• Develop robust RAG pipelines including ingestion, chunking, embeddings, retrieval, and reranking
• Implement intelligent model routing based on cost, latency, and task complexity
• Create prompt frameworks, prompt libraries, and evaluation pipelines
• Apply guardrails for safety, accuracy, and compliance

Architect Agentic AI Systems

• Design and build multi-agent systems with orchestration, memory, and tool usage
• Enable agents to interact with enterprise platforms and execute multi-step workflows
• Develop tool libraries using Python, APIs, and enterprise integrations
• Implement intent classification, routing, and escalation logic

Own the AI Engineering Stack

• Build and maintain AI workloads on Databricks including MLflow, model serving, and pipelines
• Write production-grade Python across APIs, automation, and AI orchestration
• Manage CI/CD pipelines and repository structure using GitLab
• Deliver reliable, scalable, and observable AI systems in production

Integrate AI Into Business Systems

• Connect AI solutions to ERPs, HR systems, collaboration tools, and project platforms
• Build APIs, connectors, and event-driven pipelines using Azure Functions
• Enable real-time data ingestion and AI-driven decisioning inside workflows

Drive Standards, Governance, and Reliability

• Establish best practices across AI development, deployment, and monitoring
• Ensure systems meet security, privacy, and compliance requirements
• Monitor performance including latency, uptime, and cost efficiency

If you have deep experience with GenAI, LLMs, RAG pipelines, and agentic systems in real production environments, this role is built for you., * 7+ years in AI/ML engineering, solution architecture, or software engineering

• Proven experience building and deploying production AI systems

Deep Technical Expertise In:

• GenAI and LLM systems including prompt engineering and evaluation
• RAG pipelines at scale including hybrid search, reranking, and optimization
• Model routing strategies with fallback logic and cost optimization
• Agentic AI frameworks such as LangChain, LlamaIndex, AutoGen or similar
• Databricks ecosystem including MLflow and production pipelines
• Python engineering for APIs, automation, and AI workflows
• CI/CD and GitLab for AI solution lifecycle management
• APIs and integrations across enterprise platforms
• Vector databases and semantic search

What Sets You Apart

• You have deployed RAG systems in real-world production environments
• You have built intelligent routing systems beyond simple model switching
• You have developed agent-based systems that execute real business workflows
• You are comfortable owning systems end to end, from architecture to production reliability

Why This Role

• High-impact role shaping enterprise AI strategy and execution
• Full ownership of modern AI architecture and delivery
• Opportunity to work on cutting-edge GenAI and agentic systems
• Strong compensation and comprehensive benefits
• Collaborative, fast-moving environment with real investment in AI innovation