Job Openings Agentic AI Solution Architect

About the job 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

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