AI Engineer L2

AI Engineer

JBS Alpha is Jaffer Business Systems' dedicated AI and emerging technology practice. We build AI-powered products, deliver AI engineering and integration services, and help organizations adopt artificial intelligence responsibly across both cloud and on-premises environments. Our clients operate across highly regulated and data-sensitive industries, requiring secure, scalable, and compliant AI solutions. Whether deployed in modern cloud platforms or air-gapped enterprise environments, our solutions are designed to run wherever the data needs to live.

At JBS Alpha, we combine product innovation, enterprise consulting, and AI engineering to solve real-world business challenges using Generative AI, Machine Learning, and intelligent automation.

Role Overview

We are looking for a Mid to Senior AI Engineer to design, build, and deploy production-grade AI solutions from concept to implementation. This is a hands-on engineering role that spans Generative AI, Machine Learning, MLOps, and enterprise software development.

You will work across product development, consulting engagements, and client implementations, building AI systems that operate reliably in both cloud and on-premises environments. The ideal candidate enjoys solving complex technical problems, building scalable AI applications, and working directly with clients to transform business challenges into practical AI solutions.

Location

Karachi, Pakistan

Business Unit / Department

JBS Alpha – AI & Emerging Technologies

Mode

Onsite / Hybrid

Employment Type

Full-Time

Key Responsibilities

AI & Model Development

• Design and develop Generative AI applications, including Retrieval-Augmented Generation (RAG), LLM-powered assistants, and autonomous AI agents.
• Build solutions using both API-based and self-hosted/open-source language models.
• Fine-tune and optimize AI models using techniques such as prompt engineering, embeddings, instruction tuning, LoRA, and PEFT.
• Develop classical Machine Learning models for forecasting, recommendation systems, anomaly detection, classification, and predictive analytics.
• Build evaluation frameworks to measure model quality, latency, cost, reliability, and safety before production deployment.
• Continuously research and evaluate emerging AI technologies to improve solution capabilities.
  
  
  

Engineering & Deployment

• Productionize AI models through scalable APIs and enterprise-grade services.
• Deploy AI solutions across Azure, AWS, OCI, GCP, and on-premises environments.
• Design secure deployments for air-gapped and regulated enterprise environments.
• Develop and maintain MLOps pipelines including:
• CI/CD
• Model versioning
• Monitoring
• Drift detection
• Automated retraining
• Observability
  
• Optimize model inference using quantization, batching, caching, GPU optimization, and efficient resource utilization.
• Collaborate with software engineers to integrate AI capabilities into enterprise applications.
  
  

Client Delivery & Consulting

• Engage directly with clients to understand business problems and identify AI opportunities.
• Translate business requirements into scalable AI solution architectures.
• Support technical pre-sales by contributing to proposals, demonstrations, architecture documentation, and effort estimation.
• Prepare infrastructure and hardware recommendations for enterprise AI deployments.
• Deliver reusable AI components and accelerators that strengthen JBS Alpha's portfolio.
• Produce clear technical documentation and facilitate knowledge transfer sessions.
• Mentor junior engineers and contribute to technical leadership across engagements.
  
  
  

Product Innovation

• Collaborate with business units to identify and validate new AI product ideas.
• Design rapid prototypes and Proof of Concepts (PoCs) for emerging technologies.
• Evaluate commercial opportunities for AI products and accelerators.
• Contribute to the continuous growth of JBS Alpha's AI product portfolio.
  
  
  

Required Skills

Programming

• Python
• SQL
• REST APIs
• Linux
• Git
• Software Engineering Best Practices
  
  
  

Generative AI

• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Prompt Engineering
• Embeddings
• Vector Databases
• LangChain
• LlamaIndex
• AI Agent Frameworks
  
  
  

Machine Learning

• PyTorch
• TensorFlow
• Scikit-learn
• Pandas
• Model Evaluation
• Feature Engineering
  
  
  

Cloud & Deployment

• Docker
• Kubernetes (preferred)
• Azure
• AWS
• OCI
• Google Cloud Platform
  
  
  

MLOps

• CI/CD Pipelines
• MLflow or equivalent
• Model Monitoring
• Drift Detection
• Model Versioning
  
  
  

Databases

• SQL
• Enterprise Data Pipelines
• Vector Databases
  
  
  

Qualifications

• Bachelor's degree in Computer Science, Artificial Intelligence, Software Engineering, Data Science, or a related discipline.
• Master's degree is an advantage.
• 4–8+ years of experience in Software Engineering, Machine Learning, or AI Engineering.
• Minimum 2 years of experience deploying AI or ML solutions into production.
• Proven experience developing enterprise AI applications using LLMs.
• Strong understanding of the complete Machine Learning lifecycle.
• Experience deploying containerized applications using Docker.
• Experience with at least one major cloud platform (Azure, AWS, OCI, or GCP).
• Excellent analytical, problem-solving, and communication skills.
  
  
  

Preferred Qualifications

• Experience deploying self-hosted LLMs using vLLM, Ollama, Triton, or similar technologies.
• Experience working with air-gapped or on-premises enterprise deployments.
• Hands-on experience with LoRA, QLoRA, PEFT, quantization, and model optimization.
• Kubernetes and Infrastructure-as-Code experience.
• GPU infrastructure management experience.
• Experience in consulting or client-facing delivery environments.
• Knowledge of ERP, Finance, Supply Chain, Manufacturing, or Enterprise Systems.
• Azure AI Engineer, AWS Machine Learning, OCI AI, or equivalent cloud certifications.
  
  
  

Career Level Expectations

Mid-Level AI Engineer

• Independently develops AI features and services.
• Owns well-defined technical components from development through deployment.
• Collaborates effectively within engineering teams while receiving guidance on architecture and design.
  
  
  

Senior AI Engineer

• Leads end-to-end AI solution architecture and technical delivery.
• Drives client engagements, technical pre-sales, and implementation strategy.
• Establishes engineering standards and mentors junior engineers.
• Influences the technical direction of products and consulting projects.
  
  
  

Why Join JBS Alpha?

At JBS Alpha, you'll have the opportunity to build production-ready AI solutions that create measurable business impact—not just prototypes. You'll work with cutting-edge technologies across Generative AI, Machine Learning, cloud infrastructure, and enterprise software while solving complex challenges for clients across industries and regions.

We invest in continuous learning through certifications, mentorship, and hands-on experience, providing a clear path for growth from Mid-Level Engineer to Senior Technical Leadership. As part of JBS Alpha, you'll collaborate with a team that is shaping the future of AI products, consulting, and intelligent enterprise solutions.

Note:

Please add the following to your CV: A brief note describing one AI system you have successfully shipped to production, highlighting the key trade-offs you navigated (e.g., model selection, cost vs. performance, latency, security, scalability, or deployment constraints). Additionally, include links to any relevant GitHub repositories, portfolio, technical blogs, demos, or production projects.