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GEN AI Engineer

tkxel

Lahore

On-site

PKR 1,200,000 - 1,800,000

Full time

Today
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Job summary

A tech company is looking for a skilled AI Developer in Lahore, Pakistan. The role focuses on developing and maintaining data pipelines for generative AI, requiring expertise in Python and AI/ML libraries, as well as cloud deployment experience. Candidates should have a relevant degree and 2-3 years of experience. Join an innovative team tackling complex AI challenges.

Qualifications

  • 2-3 years of professional software development experience focused on AI/ML.
  • Design and implement scalable backend services.
  • Strong understanding of software engineering principles.

Responsibilities

  • Design, build, and maintain robust data and model pipelines for generative AI.
  • Develop, train, and fine-tune generative models.
  • Architect and implement RAG systems for enhanced model accuracy.

Skills

Expert proficiency in Python
Experience with AI/ML libraries (e.g., PyTorch, TensorFlow)
Prompt Engineering for large language models (LLMs)
Familiarity with performance profiling and efficient model serving
Experience deploying models in a cloud environment (AWS, GCP, Azure)

Education

Bachelor's or Master's degree in Computer Science, AI, ML

Tools

Django
Flask
FastAPI
AWS SageMaker
Docker
Kubernetes
Job description
Key Responsibilities
  • End-to-End Pipeline Development: Design, build, and maintain robust, scalable, and efficient data and model pipelines for generative AI applications, encompassing data ingestion, preprocessing, training, fine‑tuning, and inference.
  • Model Training & Fine‑Tuning: Develop, train, and fine‑tune state‑of‑the‑art generative models (LLMs, LVMs, etc.) using advanced techniques such as Parameter‑Efficient Fine‑Tuning (PEFT/P‑Tuning/LoRA) and Reinforcement Learning from Human Feedback (RLHF).
  • Retrieval‑Augmented Generation (RAG): Architect, implement, and optimize RAG systems by integrating vector databases (e.g., Pinecone, Milvus, Weaviate, pgvector) to enhance model accuracy and reduce hallucinations.
  • Model Deployment: Package, containerize, and deploy models into production using modern MLOps and deployment tools like AWS SageMaker, Kubernetes, or similar platforms, ensuring reliability and scalability.
  • Multi‑Modal & Agentic Development: Research and prototype multi‑modal (text, image, audio) AI solutions and develop agentic systems capable of planning and executing complex tasks.
Qualifications (Required)
  • Education: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
  • Experience: 2‑3 years of professional software development experience with a strong focus on AI/ML.
  • Design and implement scalable backend services using frameworks (Django, Flask, FastAPI).
  • Programming: Expert proficiency in Python and strong experience with AI/ML libraries (e.g., PyTorch, TensorFlow, Hugging Face Transformers).
  • Generative AI Fundamentals: Proven hands‑on experience in at least three of the following:
    • Prompt Engineering for large language models (LLMs).
    • Building applications using open‑source models from HuggingFace.
    • Implementing RAG architectures using vector databases.
    • Fine‑tuning models using PEFT methods (e.g., LoRA, Adapters).
    • Diffusion models, embeddings orchestration.
  • Familiarity with performance profiling, efficient model serving, and hardware‑aware design (e.g., GPU utilization, quantization).
  • Cloud & Deployment: Solid experience deploying and managing models in a cloud environment (AWS, GCP, or Azure). Direct experience with AWS SageMaker is a significant plus.
  • Pipeline integration knowledge with web apps, for instance, Python and Ruby on Rails web applications.
  • Software Engineering: Strong understanding of software engineering principles, design patterns, and writing production‑quality, maintainable code (version control, testing, debugging).
Qualifications (Preferred)
  • Experience with RLHF and evaluating human preferences for model alignment.
  • Practical knowledge of multi‑modal models (e.g., CLIP, FLAN‑T5, Vision Transformers).
  • Experience building agentic workflows (e.g., using LangChain, LlamaIndex, or custom frameworks).
  • Familiarity with containerization (Docker) and orchestration (Kubernetes).
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