AI Engineer

Tkxel LLC · Lahore, Lahore District; Punjab, Pakistan

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