Agentic AI Solution Architect

About the position

We are looking for a senior, hands-on Agentic AI Solution Architect who can consult, design, and finalize the end-to-end solution architecture and scope of work for an enterprise-grade Agentic AI platform. Location: DHA Phase 4 Lahore, Pakistan Timings: 5pm-2am PKT (Fulltime, onsite) This role is NOT about coding everything. It is about: Asking the right questions Defining what should be built vs what should not Designing a secure, scalable, cloud-native agentic architecture Producing clear solution artifacts that engineering teams can execute on What You Will Own (Scope of Work) Business Discovery & Problem Framing Work with stakeholders to: Understand current processes, pain points, and automation opportunities Identify where agentic AI truly adds value vs simple automation Convert ambiguous ideas into: Clear use cases Agent responsibilities Success metrics (accuracy, cost, latency, risk) Deliverables: Problem statements Use-case prioritization Agent responsibility matrix Agentic AI System Design Design multi-agent workflows including: Task planning agents Reasoning agents Execution agents Validation / guardrail agents Decide when to use: Deterministic workflows vs agent-driven flows Human-in-the-loop vs autonomous execution Deliverables: Agent interaction diagrams Decision trees & control flows Cloud & AI Architecture (AWS-Centric) You will design (not just recommend) the architecture using: Core Stack Amazon Bedrock Model selection strategy (Claude, Nova, Titan, etc.) Prompt orchestration & guardrails Amazon Nova Agent orchestration & reasoning layers AWS Lambda Event-driven execution Tool calling by agents Amazon Textract Document ingestion & structured extraction Amazon Bedrock Data Automation (BDA) Knowledge grounding Vectorization & retrieval OpenAI models Use-case based comparison vs Bedrock models Hybrid model strategy (cost, latency, accuracy) Deliverables: High-level architecture diagram Data flow & security model Model selection rationale Tooling, Integrations & Data Strategy Define how agents will: Call internal tools Invoke APIs Query databases Handle documents & unstructured data Design RAG vs Agentic Retrieval strategy Define: Prompt versioning Memory (short-term vs long-term) Context boundaries Deliverables: Tool invocation strategy Data & memory architecture Integration map Governance, Security & Risk Controls Define: Role-based access Prompt & output guardrails Audit logging Cost controls Address: Hallucination risks Data leakage Model drift Deliverables: AI governance checklist Risk mitigation plan Scope Definition & Delivery Blueprint This is the most critical output. You will: Break the solution into phases Phase 1: MVP Phase 2: Scale Phase 3: Autonomy & optimization Define: In-scope vs out-of-scope Team roles required (Dev, MLOps, Cloud, QA) Time & effort estimates (high-level) Deliverables: Final Scope of Work (SoW) Phased roadmap Delivery-ready architecture pack Required Profile (Non-Negotiable) Background 8–12+ years experience across: Solution Architecture AI/ML systems Automation platforms Has designed systems, not just implemented them AI & Agentic Expertise Proven experience with: LLM-based systems Multi-agent orchestration Prompt engineering at scale Strong understanding of: When NOT to use agents Trade-offs between agents, workflows, and APIs Cloud & AWS Deep hands-on knowledge of: AWS (Lambda, IAM, S3, VPC) Amazon Bedrock ecosystem Event-driven architectures Able to reason about cost, latency, and scalability Business & Communication Can: Translate business problems into technical solutions Push back diplomatically on unrealistic expectations Communicate clearly with executives and engineers Nice to Have (Strong Plus) Previous consulting or pre-sales architecture experience Experience designing AI CoE or platform teams Familiarity with: LangGraph / LangChain Agent frameworks Enterprise RPA + AI convergence Experience in regulated or enterprise environments