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AI/ML Engineer
Career Insights for Machine Learning Engineer
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Based on Pennsylvania data
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What they do
A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
$126,339 / year median in Pennsylvania
Job Description
- Design, develop, and deploy intent classification and intent detection models using LLMs and traditional NLP methods.
- Build and optimize Natural Language Generation pipelines for chatbot responses, summarization, content creation, and knowledge grounding.
- Architect and implement LangChain and LangGraph applications for LLM-driven workflows, including autonomous agents and RAG systems.
- Develop scalable machine learning pipelines using AWS services such as Amazon SageMaker, AWS Lambda, Amazon Bedrock, AWS Step Functions, Amazon DynamoDB, and Amazon Athena.
- Integrate and fine-tune foundation models through AWS Bedrock, including Amazon Titan, Anthropic Claude, and Meta Llama.
- Collaborate with product managers, ML researchers, and backend engineers to translate business requirements into robust AI solutions.
- Lead experimentation and A/B testing activities and continuously evaluate deployed ML models.
- Contribute to MLOps, model governance, responsible AI, and engineering best practices.
- Provide technical guidance and mentorship to other ML engineers. Required Qualifications
- 7+ years of experience in machine learning, with strong focus on NLP and Generative AI.
- Strong experience building and deploying intent detection, text classification, sequence tagging, and entity recognition models.
- Strong proficiency with LangChain, LangGraph, vector databases such as FAISS or Pinecone, and LLM workflow orchestration.
- Deep hands-on experience with AWS Bedrock, Amazon SageMaker, AWS Lambda, Amazon DynamoDB, AWS Step Functions, and related AWS services.
- Experience working with open-source LLMs such as LLaMA, Mistral, or Falcon and/or commercial APIs such as Claude or GPT-4.
- Strong Python development skills.
- Experience with machine learning frameworks such as PyTorch, Hugging Face Transformers, and scikit-learn.
- Strong understanding of MLOps practices, including model versioning, ML CI/CD, monitoring, and auto-scaling.
- Bachelor's or Master's degree in Computer Science, Data Science, or a related field. Preferred Qualifications
- Experience implementing RAG systems at scale.
- Experience with vector search technologies such as Amazon OpenSearch, Pinecone, or Weaviate.
- Experience with streaming data processing technologies such as AWS Kinesis or Kafka.
- Contributions to open-source AI/ML or NLP projects.