AI Applications Architect, AI Services

Key Responsibilities

• Architecture & Technical Leadership 
• Design and own cloud-native architectures (AWS/Azure) for agentic AI workloads using Kubernetes/EKS, Terraform, Docker, serverless APIs, AWS Batch, and async orchestration frameworks (Celery, Step Functions, EventBridge, StoneBranch). 
• Define agentic system patterns using LangChain, LangGraph, Autogen, LlamaIndex, Pinecone, and other multi-agent frameworks; ensure consistency of prompt/tool design, memory/state handling, and workflow orchestration. 
• Architect vector database, RAG, embeddings pipelines, and model-serving endpoints (LLM/SLM) with strong emphasis on scalability and latency management. 
• Establish platform-wide standards for API gateway patterns, identity and auth (OAuth2, Cognito, Vault), secrets management, event contracts/schemas, and data governance. 
• Ensure holistic observability across multi-agent systems: tracing, metrics, logging, SLO/SLA definitions, synthetic checks, and incident response playbooks. 
• Lead architecture reviews, threat modeling, and performance benchmarking for agentic workloads. 


• Team Leadership & Technical Mentorship 
• Guide engineering teams through architectural decisions, distributed design principles, and production-readiness standards. 
• Mentor engineers in Kubernetes/EKS, async programming, multi-agent orchestration, cloud-native development, and responsible AI practices. 
• Provide input on hiring, onboarding, and talent development to grow AHEAD’s agentic engineering bench. 


• Delivery Excellence & Continuous Improvement 
• Partner with Delivery Leads to ensure architecture is executable, scalable, and aligned with timelines. 
• Champion automation, IaC, CI/CD, model deployment workflows, runbooks, and platform governance. 
• Lead sprint-level architectural alignment, backlog refinement, retrospectives, and post-incident reviews. 


• Stakeholder Engagement & Strategic Alignment 
• Work with Product Owners and client stakeholders to shape roadmaps, define technical scope, and convert ambiguous problem statements into actionable designs. 
• Communicate architectural decisions clearly to both technical and business audiences, balancing constraints, risks, and tradeoffs. 
• Embed platform security, compliance, cost optimization, and data integrity into all architectural decisions. 

Qualifications

• Required 
• 6+ years designing and delivering cloud-native, event-driven, or distributed architectures at scale (AWS/Azure). 
• Deep hands-on experience with: 
• Kubernetes/EKS, Docker, Terraform, and cloud infrastructure patterns 
• Python, FastAPI, async frameworks, serverless APIs 
• Vector DBs (Pinecone, Elasticsearch, pgvector) and RAG/LLM integration workflows 
• Agentic AI frameworks (LangChain, LangGraph, Autogen, CrewAI, LlamaIndex) 
• Strong knowledge of security, identity, devsecops pipelines, and secrets management in cloud environments. 
• Proven leadership experience guiding engineering teams, performing code/design reviews, and enforcing architectural best practices. 
• Excellent communication, stakeholder alignment, and documentation skills. 


• Preferred 
• Experience operating LLMs/SLMs in production (NIMs, Bedrock, OpenAI, Azure OpenAI). 
• Experience with GPU clusters, inference optimization, or model-serving architectures (Ray, Triton, KServe). 
• Consulting or client-facing architecture experience. 


•  Success Metrics 
• Delivery of resilient, scalable architectures for agentic solutions with measurable reliability and performance. 
• Clear architectural guardrails and reusable patterns that accelerate delivery across teams. 
• High engineering team engagement, skill growth, and adoption of architecture best practices. 
• Positive client feedback, successful roadmap execution, and strong alignment to business outcomes. 
• Contribution to AHEAD’s standing as the market leader in enterprise agentic AI engineering.