We are looking for a hands-on AI/ML Engineer who can take architecture and solution designs and actually build them writing code wiring up agents integrating AWS AI services and shipping working systems.
This role is NOT about designing scope or strategy. Its about execution: turning specs diagrams and use cases into functioning agentic pipelines.
What You will Do
Agent Development
Build and deploy multi-agent workflows (task planning reasoning execution and validation/guardrail agents) based on designs handed off by the architecture team
Implement tool-calling function-calling and structured outputs for LLM agents
Work with agent frameworks such as LangGraph / LangChain (or equivalent) to implement orchestration logic
AI Platform & Model Integration
Build with LLM platforms such as Amazon Bedrock OpenAI or Mistral model invocation prompt orchestration and guardrails
Implement agent orchestration logic (e.g. using Amazon Nova or framework-based orchestration like LangGraph)
Write and deploy serverless functions (AWS Lambda or equivalent) for event-driven agent execution and tool calling
Integrate OCR/document-understanding tools for ingestion and structured data extraction e.g. Mistral OCR Amazon Textract or similar based on cost/accuracy trade-offs
Build knowledge grounding and retrieval pipelines (vectorization embeddings RAG) using tools like Bedrock Knowledge Bases open-source vector DBs or equivalent
Compare and combine models across providers (OpenAI Bedrock Mistral etc.) to pick the right tool for cost latency and accuracy not locked into a single vendor
Integration & Data Work
Build API integrations database queries and internal tool connectors for agents to call
Implement RAG pipelines (retrieval-augmented generation) and agentic retrieval flows
Handle prompt versioning short-term/long-term memory implementation and context management in code
Quality Testing & Iteration
Write tests and evaluation scripts to catch hallucinations drift and failure modes
Debug agent behavior trace execution logs and iterate on prompts/logic based on real output
Implement basic guardrails logging and cost-tracking as instructed by governance guidelines
Non-Negotiable (This Matters More Than Any Specific Tool)
We will prioritize strong fundamentals and real problem-solving ability over exposure to specific frameworks or buzzwords. Specifically you must have:
Rock-solid programming fundamentals data structures algorithms clean code debugging skills that hold up under pressure
Genuine problem-solving ability can break down an ambiguous half-defined problem into logical steps without being told exactly what to do
Fast structured learning ability can pick up a new framework API or AWS service in days not weeks because the fundamentals are solid
First-principles thinking when something breaks can reason from how it actually works rather than guessing or copy-pasting fixes
Ownership mentality doesnt stop at it runs pushes until it actually works correctly and handles edge cases
Candidates who are strong on fundamentals but light on AI-specific experience are preferred over candidates who only know a specific framework tutorial-deep. We can teach Bedrock LangGraph or Nova. We cannot teach how to think.
What You Bring (Required)
2 years of hands-on software development experience
Strong Python skills with genuine understanding of the language (not just syntax)
Solid grasp of core CS fundamentals: data structures algorithms complexity debugging methodology
Some practical exposure to LLM APIs (OpenAI Anthropic Bedrock or similar) depth less important than proof you can learn this space quickly
Working knowledge of at least one major cloud platform (AWS preferred: Lambda IAM S3) GCP/Azure equivalents also fine
Comfortable working with APIs REST integrations and structured data (JSON databases)
Familiarity with backend development building/consuming REST APIs working with databases understanding request/response lifecycles basic auth and service-to-service communication (e.g. FastAPI Flask Node/Express or similar)
Debugging mindset comfortable reading logs tracing failures and fixing broken behavior systematically rather than by trial and error
Ability to take a spec/diagram from a senior architect and turn it into working code without hand-holding
Bonus not required: experience with an agent framework (LangChain LangGraph CrewAI etc.) wed rather hire strong fundamentals and teach this than hire framework familiarity without the fundamentals
Nice to Have
Experience with vector databases (Pinecone OpenSearch FAISS etc.)
Exposure to OCR/document-AI tools (Mistral OCR Amazon Textract or similar)
Familiarity with RAG pipeline construction
Experience in a startup or fast-shipping environment
Basic understanding of prompt engineering best practices
What This Role Is NOT
Not a solution architecture or client-facing consulting role
Not responsible for scoping SoW creation or high-level roadmap decisions
Not a prompt-only role this requires real coding ability
Location: DHA Phase 4 Lahore Pakistan (Onsite) Timings: 5pm2am PKT (Full-time) Experience: 2 years About the Role We are looking for a hands-on AI/ML Engineer who can take architecture and solution designs and actually build them writing code wiring up agents integrating AWS AI services and shipping...
Location: DHA Phase 4 Lahore Pakistan (Onsite)
Timings: 5pm2am PKT (Full-time)
Experience: 2 years
About the Role
We are looking for a hands-on AI/ML Engineer who can take architecture and solution designs and actually build them writing code wiring up agents integrating AWS AI services and shipping working systems.
This role is NOT about designing scope or strategy. Its about execution: turning specs diagrams and use cases into functioning agentic pipelines.
What You will Do
Agent Development
Build and deploy multi-agent workflows (task planning reasoning execution and validation/guardrail agents) based on designs handed off by the architecture team
Implement tool-calling function-calling and structured outputs for LLM agents
Work with agent frameworks such as LangGraph / LangChain (or equivalent) to implement orchestration logic
AI Platform & Model Integration
Build with LLM platforms such as Amazon Bedrock OpenAI or Mistral model invocation prompt orchestration and guardrails
Implement agent orchestration logic (e.g. using Amazon Nova or framework-based orchestration like LangGraph)
Write and deploy serverless functions (AWS Lambda or equivalent) for event-driven agent execution and tool calling
Integrate OCR/document-understanding tools for ingestion and structured data extraction e.g. Mistral OCR Amazon Textract or similar based on cost/accuracy trade-offs
Build knowledge grounding and retrieval pipelines (vectorization embeddings RAG) using tools like Bedrock Knowledge Bases open-source vector DBs or equivalent
Compare and combine models across providers (OpenAI Bedrock Mistral etc.) to pick the right tool for cost latency and accuracy not locked into a single vendor
Integration & Data Work
Build API integrations database queries and internal tool connectors for agents to call
Implement RAG pipelines (retrieval-augmented generation) and agentic retrieval flows
Handle prompt versioning short-term/long-term memory implementation and context management in code
Quality Testing & Iteration
Write tests and evaluation scripts to catch hallucinations drift and failure modes
Debug agent behavior trace execution logs and iterate on prompts/logic based on real output
Implement basic guardrails logging and cost-tracking as instructed by governance guidelines
Non-Negotiable (This Matters More Than Any Specific Tool)
We will prioritize strong fundamentals and real problem-solving ability over exposure to specific frameworks or buzzwords. Specifically you must have:
Rock-solid programming fundamentals data structures algorithms clean code debugging skills that hold up under pressure
Genuine problem-solving ability can break down an ambiguous half-defined problem into logical steps without being told exactly what to do
Fast structured learning ability can pick up a new framework API or AWS service in days not weeks because the fundamentals are solid
First-principles thinking when something breaks can reason from how it actually works rather than guessing or copy-pasting fixes
Ownership mentality doesnt stop at it runs pushes until it actually works correctly and handles edge cases
Candidates who are strong on fundamentals but light on AI-specific experience are preferred over candidates who only know a specific framework tutorial-deep. We can teach Bedrock LangGraph or Nova. We cannot teach how to think.
What You Bring (Required)
2 years of hands-on software development experience
Strong Python skills with genuine understanding of the language (not just syntax)
Solid grasp of core CS fundamentals: data structures algorithms complexity debugging methodology
Some practical exposure to LLM APIs (OpenAI Anthropic Bedrock or similar) depth less important than proof you can learn this space quickly
Working knowledge of at least one major cloud platform (AWS preferred: Lambda IAM S3) GCP/Azure equivalents also fine
Comfortable working with APIs REST integrations and structured data (JSON databases)
Familiarity with backend development building/consuming REST APIs working with databases understanding request/response lifecycles basic auth and service-to-service communication (e.g. FastAPI Flask Node/Express or similar)
Debugging mindset comfortable reading logs tracing failures and fixing broken behavior systematically rather than by trial and error
Ability to take a spec/diagram from a senior architect and turn it into working code without hand-holding
Bonus not required: experience with an agent framework (LangChain LangGraph CrewAI etc.) wed rather hire strong fundamentals and teach this than hire framework familiarity without the fundamentals
Nice to Have
Experience with vector databases (Pinecone OpenSearch FAISS etc.)
Exposure to OCR/document-AI tools (Mistral OCR Amazon Textract or similar)
Familiarity with RAG pipeline construction
Experience in a startup or fast-shipping environment
Basic understanding of prompt engineering best practices
What This Role Is NOT
Not a solution architecture or client-facing consulting role
Not responsible for scoping SoW creation or high-level roadmap decisions
Not a prompt-only role this requires real coding ability