GEN AI Engineer

2 weeks ago

Lahore, Punjab, Pakistan Tkxel Full time 1,200,000 - 3,600,000 per year
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).

  • Experience with distributed training frameworks (e.g., DeepSpeed, FSDP).



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