Job Overview
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Job Description
Qualifications
AWS Services: Proficiency in SageMaker, Glue, Lambda, EC, EMR, Athena, Bedrock, and Redshift ML & AI Expertise: Experience with supervised/unsupervised learning, deep learning (CNNs, RNNs), and LLMs (, GPT, BERT, RAG, fine-tuning, LLMOps) .
Programming: Strong in Python, SQL, PySpark; familiar with ML frameworks like Scikit-learn, TensorFlow, PyTorch MLOps: Skilled in CI/CD, model deployment using MLflow, FastAPI, Docker, and monitoring pipelines Data Engineering: Building scalable data pipelines, feature engineering, and data lakehouse architectures eam Context Current team: Data Scientists working on a range of projects (static ML, LLMs, NLP, data engineering). This hire will be critical in helping operationalize the team's work (MLOps, pipelines, deployment). Role is both hands-on technical and strategic: build pipelines, manage model operations, and guide best practices. Key Requirements Strong hands-on Python development is essential (no one else currently on the team can take this on). Must have ML engineering + MLOps experience (pipelines, deployment, guardrails). Strong communication skills are required to explain approaches, options, and best practices to the team. Role will involve a mix of building new pipelines and enhancing existing ones. Candidate Profile Looking for a senior-level practitioner who can also guide junior resources as the team grows. Needs to be a number one hire” — someone who can set the tone for ML engineering on the team.
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