Lead ML Engineer (Onsite, Lahore, Remittance Salary)

Requirements:

Bachelors or Masters degree in Computer Science, Data Science, Statistics, or a related field.
4 to 6 years of hands-on experience in machine learning model development, including NLP, NER, and large language models.
Strong proficiency in Python and experience with machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn.
Proven experience in developing models using BERT, LangChain, RAG, and related modern AI frameworks.
Strong understanding of neural networks, deep learning concepts, and their underlying mathematical principles.
Experience deploying machine learning models into production environments.
Experience developing APIs and working with backend frameworks such as FastAPI, Django, or Flask.
Experience with Docker and implementing CI/CD pipelines.
Experience building and managing MLOps pipelines and using version control tools such as DVC.
Familiarity with image processing and computer vision techniques.
Experience with auto-scaling systems and cloud-based deployments.
Experience working with GANs, Stable Diffusion, or other generative models.
Experience with 3D neural networks and meta-learning techniques.
Experience deploying machine learning models on embedded systems such as Raspberry Pi.

Responsibilities:

Design, develop, and implement advanced machine learning and deep learning models, with a focus on natural language processing (NLP), named entity recognition (NER), and large language models (LLMs) such as BERT.
Build AI agents and multi-agent workflows using frameworks such as LangChain, LlamaIndex, and AutoGen, and develop Retrieval Augmented Generation (RAG) applications.
Develop and optimize neural network architectures to ensure models are efficient, scalable, and production-ready.
Integrate multimodal capabilities by incorporating image processing techniques, including image segmentation, object detection, and facial recognition.
Train, fine-tune, and optimize large language models using techniques such as LoRA, QLoRA, and quantization.
Develop and deploy machine learning models into production environments, ensuring reliability and performance. Build custom APIs using frameworks such as FastAPI, Django, or Flask to support AI and ML applications.
Containerize applications using Docker and implement CI/CD pipelines to enable efficient deployment and updates.
Establish and manage MLOps pipelines using tools such as Data Version Control (DVC) to ensure proper versioning and reproducibility.
Conduct research to stay current with emerging AI, machine learning, and deep learning trends and technologies.
Document methodologies, models, and technical processes, and prepare reports and presentations for technical and non-technical stakeholders.
Collaborate with cross-functional teams to integrate AI solutions into the organizations products and systems.

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