What are the responsibilities and job description for the TensorFlow AI Developer/Architect position at WASTELINQ, LLC?

Company Description

WASTELINQ, Inc. provides web-based software solutions for waste industry customers and service providers. The WASTELINQ-Enterprise application offers a range of features to help streamline waste management operations. With 7 years of development and testing, our software is designed to meet the unique needs of waste brokers, TSDFs, consultants, and industrial waste service providers. We are seeking an AI / ML Engineer to assist us in adding intelligence to our waste management systems. You will work on advanced knowledge retrieval pipelines, agentic tools for workflow automation, and AI powered methods for ensuring regulatory compliance.

Key Responsibilities :

Develop and fine-tune large language models for specific business applications.
Implement Retrieval-Augmented Generation (RAG) methodologies in regulatory settings.
Design, test, and deploy AI agents for operational tasks and workflow automation.
Development of chatbots that integrate into existing internal APIs to provide business functionality.
Developing unit testing and evaluation suites for LLM powered products.
Collaborate with cross-functional teams to integrate AI capabilities into existing workflows.
Deploying and maintaining models according to standard MLOps best practices.

Qualifications :

Minimum : Bachelor’s in Computer Science, Data Science, Statistics, or a related field.

Preferred : Master’s or Ph.D. in Computer Science, Data Science, Statistics, or a related field.

4 years of programming experience in Python, Java, SQL, Linux.

Track record of designing, deploying, and managing end-to-end AI solutions.

Solid understanding of RAG techniques and their real-world applications.

Extensive unit testing experience, both for python and LLM applications.

Proven experience in LlamaIndex or similar LLM frameworks for developing LLM powered products.

Experience implementing agentic approaches for workflow automations.

Familiarity with vector databases (like Milvus, Pinecone, Weaviate) for RAG implementations

Familiarity with AWS SageMaker for model training and deployment.

Experience with model versioning and experiment tracking tools (MLflow, or Weights & Biases).

Knowledge of CI / CD pipelines for ML models.

Understanding of model monitoring and observability practices.

Familiarity with feature stores and data versioning.

Experience with containerization (Docker) and AWS container services (ECS / Fargate).

Familiarity with ETL processes and experience implementing data pipelines.

Applicants must be authorized to work in the U.S. without sponsorship

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TensorFlow AI Developer/Architect

What are the responsibilities and job description for the TensorFlow AI Developer/Architect position at WASTELINQ, LLC?

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