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Remote mid-level ML/AI role (3–6 yrs) with popular title increases candidate competition.
Core ML/LLM engineering skills transfer across industries, though domain expertise can increase sensitivity.
Explicit 3–6 years plus specific LLM, MLOps, cloud and deployment skills make screening strict.
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Design, develop, train, fine-tune, and evaluate machine learning and LLM-based solutions including Generative AI applications.
Build and maintain end-to-end ML/AI pipelines covering data ingestion, processing, model development, inference, deployment, and monitoring with MLOps best practices.
Optimize models and AI applications for accuracy, latency, scalability, cost efficiency, and implement production monitoring, guardrails, and model lifecycle management.
3–6 years of hands-on experience in Machine Learning Engineering or a closely related role.
Strong proficiency in Python and experience with machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn.
Proven experience delivering Generative AI solutions including LLM fine-tuning, prompt engineering, RAG architectures, vector databases, and semantic search in production environments.
Experience with MLOps practices (model versioning, CI/CD, deployment automation) and at least one major cloud-based ML platform (AWS SageMaker, Azure ML, or Google Cloud Vertex AI).
Experienced in production-grade ML/AI pipelines and deploying Generative AI solutions from prototype to production with focus on model evaluation and optimization.
Strong cross-functional collaboration skills to translate business requirements into scalable AI/ML solutions.
Hands-on engineering approach with practical knowledge of state-of-the-art Generative AI, LLMs, RAG systems, and MLOps in dynamic, fast-paced environments.