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AI/ML Engineer

Pakistan·Karachimid

Machine Learning EngineerData

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Quick Summary

Key Responsibilities

Develop and implement machine learning models and LLMs, optimizing them for real-world deployment Fine-tune and optimize large language models (LLMs) to enhance performance and scalability Work with AI development frameworks like LangChain, PyTorch,…

Technical Tools

awsazuredjangodockerfastapiflaskkuberneteslangchainpythonpytorchdeep-learningmachine-learningsecurity-best-practices

Responsibilities

~1 min read

→Develop and implement machine learning models and LLMs, optimizing them for real-world deployment
→Fine-tune and optimize large language models (LLMs) to enhance performance and scalability
→Work with AI development frameworks like LangChain, PyTorch, Keras, etc. for AI-driven applications
→Design and develop machine learning algorithms and deep learning applications and systems
→Solve complex problems with multilayered data sets, and optimize existing machine learning libraries and frameworks
→Train and monitor machine learning systems and models as needed
→Design and implement embedding-based similarity algorithms to improve AI-powered search and retrieval
→Implement Retrieval-Augmented Generation (RAG) techniques for enhanced AI response generation
→Utilize vector databases (e.g. Chroma, Pinecone, Weaviate, etc.) to support efficient data retrieval
→Develop and deploy AI/ML pipelines
→Work with frameworks like FastAPI, Flask, or Django to integrate AI models into applications
→Collaborate with Data Engineers to integrate AI models with Vector databases and conventional Databases
→Optimize AI inference for low latency and high scalability in production environments
→Implement AI security best practices to mitigate risks like adversarial attacks
→Stay up to date with advancements in LLMs, ML, AI search, and model deployment strategies

Requirements

~1 min read

Bachelor's or master's degree in computer science, Data Science, Engineering, or a related field
Strong proficiency in Python for AI/ML development
Hands-on experience with LLMs and transformer-based architectures
Hands-on experience with ML based development
Experience implementing RAG techniques in AI applications
Familiarity with embedding models and NLP pipelines
Understanding of AI inference optimization and scalability strategies
Experience with MLOps tools is a plus
Knowledge of containerization and deployment using Docker, Kubernetes, or cloud platforms (AWS, Azure)

Offerings & Benefits: