AI Engineer

Tkxel LLC; Lahore; On-site; PKR 38,770,000 - 52,617,000; Full time

Job summary

Tkxel is seeking an Expert AI Engineer to own the design, architecture, and deployment of agentic AI systems for a bilingual enterprise platform based on large language models. You will shape autonomous, multi-agent workflows and robust retrieval pipelines with production-grade RAG.

Responsibilities include architecting planning-driven systems, orchestration across agents and tools, and durable state management with retries.

Qualifications

• Deep, demonstrated expertise in agentic AI and LLM-based applications.
• Expert Python proficiency and strong software engineering foundation.
• Proven experience architecting agentic systems with planning and orchestration.
• Hands-on experience with LangGraph, LangChain, LlamaIndex, AutoGen, or CrewAI.
• Strong command of agent state management and tool calling standards (MCP).
• Experience designing DAG/state-machine workflows with durable state and retries.
• Experience building connectors to enterprise systems and OAuth-based auth.
• Production experience with background schedulers (Celery, Airflow, Temporal).
• Hands-on with vector databases and lexical search (BM25, Elasticsearch).
• Advanced prompt engineering, grounding, and evaluation strategies.

Responsibilities

• Architect agentic systems with planning, memory, and multi-step task execution.
• Define orchestration patterns across agents, tools, and LLM calls.
• Design robust single- and multi-agent architectures with state management.
• Develop the workflow layer with versioned, durable workflows and retries.
• Build connectors to enterprise content sources and APIs with secure auth.
• Own the background processing layer including ingestion and indexing.
• Oversee RAG layer including embedding, retrieval, and grounding generation.
• Implement and tune dense, sparse, hybrid, and metadata-based retrieval.
• Define prompts, grounding, and verification to ensure accurate responses.
• Set evaluation frameworks and quality gates to improve latency and reliability.

Skills

Agentic AI

LLM apps

Python

System design

Production deployment

LangGraph

LangChain

LlamaIndex

AutoGen

CrewAI

MCP

DAG/state machine

Celery

Airflow

Temporal

Milvus

FAISS

Qdrant

pgvector

BM25

Elasticsearch

RAG

Retrieval techniques

Prompt engineering

Grounding

Evaluation frameworks

Tools

Milvus

FAISS

Qdrant

pgvector

BM25

Elasticsearch

Job description

Tkxel is a leading softwaredevelopment company located in Reston, Virginia. We are committedto develop innovative software solutions for leading enterprisesin the world, helping them grow their businesses using latesttechnology solutions.

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