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Machine Learning Engineering Lead
16 hours ago
We're looking for an experienced professional to join our team as a Machine Learning Engineer. The ideal candidate will have a strong background in AI/ML model development and deployment, as well as experience working with large-scale datasets.
Responsibilities:
- Design and develop machine learning models and AI-driven solutions to address complex business challenges.
- Collaborate with cross-functional teams to define project requirements and integrate AI/ML solutions into business workflows.
- Develop and deploy secure and scalable ML pipelines, leveraging MLOps best practices for continuous integration and deployment.
- Monitor, evaluate, and refine model performance using A/B testing, drift detection, explainability, and adversarial robustness techniques.
- Implement AI security best practices, including adversarial attack detection, model integrity verification, and bias mitigation.
- Stay updated with LLMs, generative AI, retrieval-based AI trends, and secure AI development methodologies.
- Deploy and manage AI/ML models on cloud platforms such as AWS, Azure, or GCP, ensuring compliance with secure AI governance frameworks.
- Work with vector databases (e.g., FAISS, Pinecone, Weaviate, ChromaDB) and retrieval frameworks (e.g., LangChain, LlamaIndex).
- Build and optimize secure APIs, ensuring protection against data leaks, prompt injection attacks, and unauthorized access.
Requirements:
- 2+ years of experience in AI/ML model development and deployment.
- Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Artificial Intelligence, or a related field.
- Strong programming skills in Python (preferred), R, or Java.
- Hands-on experience with ML frameworks like TensorFlow, PyTorch, or Keras.
- Expertise in natural language processing (NLP), computer vision, deep learning, and LLMs.
- Proficiency in retrieval-augmented generation (RAG), embedding models, and vector databases (FAISS, Pinecone, Weaviate, ChromaDB).
- Experience with cloud AI services (AWS SageMaker, Azure AI, Google AI Platform).
- Strong understanding of Hugging Face Transformers, Model Hub, and tokenizers for model fine-tuning and deployment.
- Familiarity with big data tools such as Hadoop, Spark, or Kafka (preferred).
- Strong understanding of adversarial ML attacks, model explainability, AI governance, and bias mitigation techniques.
- Knowledge of security best practices for AI, including prompt injection protection, data anonymization, and secure model deployment.
- Strong foundation in algorithms, data structures, and model evaluation techniques.
- Proven ability to deliver end-to-end AI/ML projects, from concept to production, with a focus on secure and responsible AI.
- Strong analytical and problem-solving skills with attention to detail.
- Excellent communication skills to collaborate with teams and present findings.
- Ability to work on-site and manage multiple projects effectively.
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