AI Developer — Onsite, Sialkot Office

FabTechSol; Sialkot; On-site; PKR 1,674,000 - 2,790,000; Full time

Job summary

A leading AI engineering company in Sialkot seeks talented developers proficient in the modern AI stack, including LLMs and MLOps. Ideal candidates have deep experience in building AI systems and can debug complex AI models, addressing issues like hallucinations and bias. The role involves working across the full lifecycle of AI projects, ensuring responsible deployment and collaboration across diverse teams. Competitive opportunities await those passionate about advancing AI solutions.

Qualifications

• Deep understanding of core ML and deep learning concepts.
• Production experience with Python and ML frameworks.
• Hands-on expertise with model versioning and CI/CD for ML.

Responsibilities

• Develop production-level AI solutions across the modern AI stack.
• Debug and enhance AI models to mitigate issues like bias and hallucinations.
• Collaborate with cross-functional teams to translate business needs into technical solutions.

Skills

Apache Spark

Hadoop

Kafka

Algorithms

Deep Learning

LLMs & NLP

Reinforcement Learning

Docker

Kubernetes

Big Data

Tools

TensorFlow

PyTorch

JAX

Scikit-learn

Job description

FabTechSol is building an A-class AI engineering team at our Sialkot office.

We need developers with deep, production-level experience across the modern AI stack — from core ML and deep learning to LLMs, MLOps, generative AI, and responsible deployment. If you understand not just how to use AI tools but how and why they break, we want to talk.

What We're Looking For

Depth beats familiarity — we hire for mastery, not buzzwords.

We're not hiring someone who has watched tutorials on GPT wrappers. We need engineers who understand model internals, can debug drift and hallucinations, build production ML pipelines from scratch, and ship AI systems that work reliably at scale.

The ideal candidate combines strong technical foundations (mathematics, algorithms, software engineering) with deep specialization in one or more areas of modern AI — whether that's LLMs, computer vision, reinforcement learning, or MLOps. You'll work across the full lifecycle: research, prototyping, training, deployment, monitoring, and iteration.

Skill Framework

You don't need to be an expert in every single item — but you should have strong coverage across these categories and deep expertise in at least two.

Core Technical Foundations

• Apache Spark, Hadoop, Kafka — pipeline design and management
• Algorithms & data structures for scalable solutions

Advanced AI & Specializations

• LLMs & NLP — tokenization, embeddings, fine-tuning (LoRA, adapters), RAG
• Prompt engineering — structured input design, hallucination mitigation
• Reinforcement learning — reward modeling, policy optimization, RLHF
• Docker, Kubernetes for scalable AI deployments
• Big data at scale — real-time streams, distributed training, feature stores

Cognitive & Problem-Solving

• First-principles thinking — breaking down novel problems from scratch
• Systems thinking — understanding how AI components interact in larger ecosystems
• Adaptability — continuously changing tools and frameworks as the field evolves
• Debugging complex models — diagnosing bias, drift, hallucinations, brittle reasoning
• Empathy & user focus — understanding real user problems worth solving
• Cross-disciplinary communication with data scientists, PMs, and engineers
• Ethics & responsible AI — bias detection, fairness, transparency, regulatory awareness
• Continuous learning mindset — stagnation is disqualifying
• Documentation & knowledge sharing — team knowledge scales beyond individuals

Team Role Matrix

• LLM/GenAI Specialist — foundation models, RAG, fine-tuning, prompt systems
• AI Product/Systems Architect — translating business problems into AI design
• AI Ethics & Safety Lead — bias auditing, safety controls, compliance

Apply for This Role

Application Form

Fill out the form below. We review every application carefully. Only candidates with verifiable AI engineering experience will be considered.

Personal Information

Experience & Engagement

Years of Experience *

Preferred Engagement *

Work Arrangement *

Availability *

Primary AI Specialization(s) * — select all that apply Core Technical Foundations Advanced AI & Specializations MLOps & Systems Engineering Cognitive & Problem-Solving Soft Skills & Collaboration Team Role Matrix

Preferred ML Stack * — select all that apply PyTorch TensorFlow JAX Scikit-learn Other

Preferred Cloud * — select all that apply AWS GCP Azure On-premise/VPS No preference

Technical Details

Key Skills * (comma-separated)

LinkedIn Profile

Hugging Face / Kaggle / Papers with Code

Production AI Projects * (2-3 required)

Research & Open-Source Contributions

Upload CV (PDF or Word, max 10MB)

Experience Confirmations *

All six must be confirmed to be considered. Check every item that applies to your actual experience.

Core Technical Foundations — I have production experience with Python, ML frameworks (TensorFlow/PyTorch), and strong mathematical fundamentals

Advanced AI & Specializations — I have built and deployed LLM-based applications, generative AI, computer vision, or reinforcement learning systems

MLOps & Systems Engineering — I have hands-on experience with model versioning, CI/CD for ML, containerized deployments, and cloud platforms (AWS/GCP/Azure)

Cognitive & Problem-Solving — I can demonstrate first-principles thinking and debug complex models (bias, drift, hallucinations) in production

Soft Skills & Collaboration — I communicate effectively across technical and non-technical teams and care about responsible AI practices

Team Role Matrix — I can identify which AI team role(s) I best fit (ML Research, MLOps, Data Engineering, LLM/GenAI, Systems Architect, or AI Ethics/Safety)