Agentic AI Solution Architect

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)

1. Business Discovery & Problem Framing
   1. Work with stakeholders to:
      1. Understand current processes, pain points, and automation opportunities
      2. Identify where agentic AI truly adds value vs simple automation
   2. Convert ambiguous ideas into:
      1. Clear use cases
      2. Agent responsibilities
      3. Success metrics (accuracy, cost, latency, risk)

Deliverables:

• Problem statements
• Use-case prioritization
• Agent responsibility matrix

1. Agentic AI System Design
   1. Design multi-agent workflows including:
      1. Task planning agents
      2. Reasoning agents
      3. Execution agents
      4. Validation / guardrail agents
   2. Decide when to use:
      1. Deterministic workflows vs agent-driven flows
      2. Human-in-the-loop vs autonomous execution

Deliverables:

• Agent interaction diagrams
• Decision trees & control flows

1. Cloud & AI Architecture (AWS-Centric)

You will design (not just recommend) the architecture using:

Core Stack

1. Amazon Bedrock
   1. Model selection strategy (Claude, Nova, Titan, etc.)
   2. Prompt orchestration & guardrails
2. Amazon Nova
   1. Agent orchestration & reasoning layers
3. AWS Lambda
   1. Event-driven execution
   2. Tool calling by agents
4. Amazon Textract
   1. Document ingestion & structured extraction
5. Amazon Bedrock Data Automation (BDA)
   1. Knowledge grounding
   2. Vectorization & retrieval
6. OpenAI models
   1. Use-case based comparison vs Bedrock models
   2. Hybrid model strategy (cost, latency, accuracy)

Deliverables:

• High-level architecture diagram
• Data flow & security model
• Model selection rationale

1. Tooling, Integrations & Data Strategy
   1. Define how agents will:
      1. Call internal tools
      2. Invoke APIs
      3. Query databases
      4. Handle documents & unstructured data
   2. Design RAG vs Agentic Retrieval strategy
   3. Define:
      1. Prompt versioning
      2. Memory (short-term vs long-term)
      3. Context boundaries

Deliverables:

• Tool invocation strategy
• Data & memory architecture
• Integration map
• Governance, Security & Risk Controls
  1. Define:
     1. Role-based access
     2. Prompt & output guardrails
     3. Audit logging
     4. Cost controls
  2. Address:
     1. Hallucination risks
     2. Data leakage
     3. Model drift

Deliverables:

• AI governance checklist
• Risk mitigation plan
• Scope Definition & Delivery Blueprint

This is the most critical output.

You will:

1. Break the solution into phases
   1. Phase 1: MVP
   2. Phase 2: Scale
   3. Phase 3: Autonomy & optimization
2. Define:
   1. In-scope vs out-of-scope
   2. Team roles required (Dev, MLOps, Cloud, QA)
   3. 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