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AI Summary
Design, develop, and deploy machine learning models and deep learning architectures for production environments. Collaborate with data engineers to build end-to-end pipelines and monitor model performance.
This is a remote position.
Key Responsibilities
Design and develop machine learning models, deep learning architectures, and NLP solutions for production use
Build and maintain end-to-end ML pipelines — from data ingestion and preprocessing through to model training, evaluation, and deployment
Integrate AI/ML models into existing applications and APIs
Fine-tune and evaluate large language models (LLMs) for specific business use cases
Collaborate with data engineers to ensure high-quality, well-structured training data
Monitor model performance in production and implement retraining strategies
Research and evaluate emerging AI frameworks, tools, and techniques
Document models, experiments, and technical decisions clearly
Required Skills & Experience
3+ years of experience in AI/ML development
Strong proficiency in Python and ML libraries such as TensorFlow, PyTorch, or scikit-learn
Experience with NLP frameworks (Hugging Face, spaCy, LangChain)
Familiarity with LLMs and prompt engineering (OpenAI, Anthropic, Gemini, etc.)
Experience deploying models via REST APIs (FastAPI, Flask)
Solid understanding of data structures, algorithms, and statistical concepts
Familiarity with cloud platforms (AWS, Azure, or GCP) and MLOps tools
Experience with vector databases (Pinecone, Weaviate, FAISS) is a plus
Nice to Have
Experience with RAG (Retrieval-Augmented Generation) architectures
Knowledge of model quantisation, distillation, or fine-tuning techniques
Familiarity with containerisation tools (Docker, Kubernetes)
Exposure to AI governance, responsible AI, or model explainability frameworks
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