Agentic AI Solution Architect

Lahore - Pakistan

Monthly Salary: Not Disclosed

Posted on: 6 hours ago

Vacancies: 1 Vacancy

Job Summary

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
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

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

Cloud & AI Architecture (AWS-Centric)

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

Core Stack

Amazon Bedrock
1. Model selection strategy (Claude Nova Titan etc.)
2. Prompt orchestration & guardrails
Amazon Nova
1. Agent orchestration & reasoning layers
AWS Lambda
1. Event-driven execution
2. Tool calling by agents
Amazon Textract
1. Document ingestion & structured extraction
Amazon Bedrock Data Automation (BDA)
1. Knowledge grounding
2. Vectorization & retrieval
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

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:

Break the solution into phases
1. Phase 1: MVP
2. Phase 2: Scale
3. Phase 3: Autonomy & optimization
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

812 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

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 PakistanTimings: 5pm-2am PKT (Fulltime onsite) This role is NOT about...

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
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

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

Cloud & AI Architecture (AWS-Centric)

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

Core Stack

Amazon Bedrock
1. Model selection strategy (Claude Nova Titan etc.)
2. Prompt orchestration & guardrails
Amazon Nova
1. Agent orchestration & reasoning layers
AWS Lambda
1. Event-driven execution
2. Tool calling by agents
Amazon Textract
1. Document ingestion & structured extraction
Amazon Bedrock Data Automation (BDA)
1. Knowledge grounding
2. Vectorization & retrieval
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

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:

Break the solution into phases
1. Phase 1: MVP
2. Phase 2: Scale
3. Phase 3: Autonomy & optimization
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

812 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