AI Engineer

Tkxel; Lahore; On-site; PKR 2,000,000 - 4,000,000; Full time

Job summary

Tkxel seeks an Expert AI Engineer to own design, architecture, and deployment of Agentic AI systems for a bilingual enterprise knowledge platform. You will drive autonomous, multi-agent workflows on top of production-grade RAG pipelines, with end-to-end ownership of architecture, quality, and performance.

Responsibilities cover architecture of agentic systems, orchestration, and robust workflow layers, including integration with enterprise APIs, background processing, and grounded generation

Qualifications

• Deep, demonstrated expertise in agentic AI and LLM-based applications, with a track record of production systems.
• Expert Python proficiency and a strong software engineering foundation, including production deployment and system design.
• Proven experience architecting agentic systems with planning, tool use, orchestration, and multi-agent coordination.
• Hands-on experience with agent frameworks such as LangGraph, LangChain, LlamaIndex, AutoGen, or CrewAI.
• Strong command of agent state management, tool/function calling, and MCP standards.
• Experience designing workflow orchestration systems with DAG/state-machine execution and durable state.
• Experience building connectors to enterprise systems and APIs with OAuth and service accounts.
• Production experience with background job and scheduling infra (Celery, Airflow, Prefect, Temporal).
• Hands-on with vector databases (Milvus, FAISS, Qdrant, pgvector) and lexical search (BM25, Elasticsearch).
• Expert command of retrieval techniques, including dense/sparse/hybrid and reranking.
• Advanced prompt engineering and grounding techniques for large language models.
• Track record deploying and scaling production-ready agentic AI pipelines.
• Strong grasp of RAG and agent evaluation methodologies; define quality standards.

Responsibilities

• Architect Agentic systems with query planning, reasoning, memory, tool use, and multi-step execution.
• Define orchestration patterns coordinating agents, retrieval, tools, and LLM calls into pipelines.
• Design robust single- and multi-agent architectures with state management and guardrails.
• Architect the workflow layer with versioned, durable, retryable steps and human-in-the-loop checks.
• Design connectors and integration layers for enterprise content sources and APIs.
• Own background processing: scheduled ingestion, index/embedding refresh, queues, and retries.
• Own and optimize the RAG layer: chunking, embedding, retrieval, reranking, grounded generation.
• Implement/dial in dense, sparse, hybrid, and metadata-based retrieval using vector DBs and BM25.
• Establish prompting, grounding, and verification to ensure citation-backed responses.
• Define evaluation frameworks and quality gates; drive performance improvements.

Skills

Python proficiency

Agentic AI

System design

Production deployment

LangGraph

LangChain

LlamaIndex

AutoGen

CrewAI

Tools

LangGraph

LangChain

LlamaIndex

AutoGen

CrewAI

Job description

We are seeking an Expert AI Engineer to own the design, architecture, and deployment of Agentic AI systems for a bilingual enterprise knowledge platform built on large language models. You will define the technical direction for autonomous, multi-agent, and multi-step workflows—incorporating planning, reasoning, memory, and tool use—on top of production-grade Retrieval-Augmented Generation (RAG) pipelines, along with the workflow, integration, and background processing layers that support them. This role carries deep end-to-end ownership of architecture, quality, and performance.

Responsibilities

• Architect Agentic systems incorporating query planning, reasoning, memory, tool use, and multi-step task execution
  

• Define orchestration patterns that coordinate agents, retrieval, tools, and LLM calls into reliable, observable autonomous pipelines
  

• Design robust single- and multi-agent architectures with state management, control flow, error recovery, and guardrails
  

• Architect the workflow layer: composable, versioned workflows combining deterministic steps, agentic branches, conditional routing, and human-in-the-loop checkpoints, with durable state, checkpointing, and retries
  

• Design the connector and integration layer for enterprise content sources and APIs, covering authentication, incremental sync, content normalization, and permission-aware retrieval
  

• Own the background processing layer, including scheduled ingestion, index and embedding refresh, job queuing and concurrency control, failure recovery, and content-freshness monitoring
  

• Own and optimize the underlying RAG layer spanning chunking, embedding, retrieval, reranking, and grounded generation
  

• Implement and tune dense, sparse, hybrid, and metadata-based retrieval using vector databases and BM25
  

• Establish prompting, grounding, and verification strategies to ensure responses remain accurate and citation-backed
  

• Define evaluation frameworks and quality gates; drive continuous improvement across task success, relevance, latency, and reliability
  

Requirements

• Deep, demonstrated expertise in agentic AI and LLM-based applications, with a track record of production systems
  

• Expert Python proficiency and a strong software engineering foundation, including production deployment and system design
  

• Proven experience architecting agentic systems with planning, tool use, orchestration, and multi-agent coordination
  

• Deep hands‑on experience with agent frameworks such as LangGraph, LangChain, LlamaIndex, AutoGen, or CrewAI
  

• Strong command of agent state management, tool/function calling, and standards such as the Model Context Protocol (MCP)
  

• Demonstrated experience designing workflow orchestration systems, including DAG or state-machine execution, durable state, retry semantics, and idempotent step design
  

• Hands‑on experience building connectors and integrations against enterprise systems and third-party APIs, including OAuth and service-account authentication, rate limiting, and permission-aware synchronization
  

• Production experience with background job and scheduling infrastructure (Celery, Airflow, Prefect, Temporal, or equivalent), including scheduled ingestion and failure recovery
  

• Hands‑on experience with vector databases (Milvus, FAISS, Qdrant, pgvector) and lexical search (BM25, Elasticsearch)
  

• Expert command of retrieval techniques, including dense, sparse, hybrid, and filtered search, plus reranking
  

• Advanced prompt engineering and grounding techniques for large language models
  

• Track record of deploying, scaling, and optimizing production-ready agentic AI pipelines
  

• Strong grasp of RAG and agent evaluation methodologies, with the ability to define quality standards