tms · 2 days ago

Sr. Machine Learning Engineer, Generative AI Applications

New York, NY

Contract

Remote

Senior Level

15+ years exp

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Responsibilities

Actively contribute to and re-use community best practices.

Monitor, debug, track, and resolve production issues.

Work with project managers to ensure that projects proceed on time and on budget.

Collaborate with Technical Product Managers to ensure proper tracking of algorithmic performance KPIs and prioritize performance improvements based on effort and impact.

Complete other responsibilities as assigned.

Qualification

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PythonSQLNLPDeep LearningPyTorchTensorflowMXNetGPU ClustersLangchainModel GuardrailsBias DetectionBias MitigationAI ApplicationsNeMoEmbedding ModelsVector DatabasesSemantic SearchRetrieval SystemsTransformersSelf-Attention MechanismsLLMsRelational DatabasesNoSQL DatabasesBig Data ToolsHadoopSparkKafkaLinuxAWSData Pipeline

Required

Minimum of seven years’ post-secondary education or relevant work experience

Bachelor's degree in mathematics, physics, computer science, engineering, statistics, or an equivalent technical discipline

Minimum of five years’ software development experience with Python and SQL

Minimum of three years’ experience building pipelines to deploy NLP and deep learning models into production in a cloud environment

Minimum three years’ experience using PyTorch, Tensorflow, or MXNet, along with optimizing code for GPU clusters

Experience building advanced workflows such as retrieval augmented generation, model chaining, dynamic prompting, PEFT/SFT, etc. using Langchain and similar tools

Experience establishing model guardrails and developing bias detection and mitigation techniques for AI applications using tools such as NeMo

Experience with various embedding models and setting up and tuning vector databases to improve performance of semantic search and retrieval systems

Understand the underlying fundamentals such as Transformers, Self-Attention mechanisms that form the theoretical foundation of LLMs

Experience working with a variety of relational SQL and NoSQL databases, big data tools: Hadoop, Spark, Kafka; a Linux environment; (AWS)

Knowledge of data pipeline and workflow management tools

Expertise in standard software engineering methodology, e.g., unit testing, test automation, continuous integration, code reviews, design documentation