Machine Learning Engineer II

Jobtailor; Massachusetts; On-site; USD 140,000 - 170,000; Full time

Job summary

Jobtailor is seeking an experienced ML/NLP Engineer to design and implement end-to-end RAG pipelines, combining chunking, embedding models, and vector databases. You will build scalable retrieval systems for large proprietary datasets and orchestrate LLM-based solutions for high-quality, context-aware responses.

You will collaborate with Product, Design, and ML Ops to automate the ML lifecycle from development to deployment in a fast-paced environment.

Qualifications

• Bachelor's degree or higher in Computer Science, Engineering, or a related field.
• 3+ years of hands-on industry experience with ML, NLP, information retrieval, and large-scale text processing.
• Experience designing, shipping, and maintaining production systems.
• Strong Python programming skills.
• Experience with PyTorch, Transformers, and HuggingFace.
• Experience with LangChain and LlamaIndex.
• Knowledge of vector databases and similarity search techniques.

Responsibilities

• Design and implement end-to-end RAG pipelines integrating chunking, embedding models, vector databases, and data retrieval agents.
• Build and optimize retrieval systems over large-scale proprietary datasets using advanced embedding techniques.
• Develop LLM-based solutions orchestrating retrieval, generation, and ranking for context-aware responses.
• Investigate challenges in vector search, chunking/indexing strategies, and unstructured data retrieval evaluation.
• Collaborate with Product and Design teams to build ML-based solutions enhancing user experiences and business objectives.
• Work with ML Operations to automate management of the full ML systems lifecycle from design to implementation.

Skills

RAG pipelines

End-to-end design

Python programming

LLM orchestration

Information retrieval

NLP

Vector databases

Embedding models

Code optimization

Education

Bachelor's degree or higher in CS/Engineering

Tools

PyTorch

Transformers

HuggingFace

LangChain

LlamaIndex

PostgreSQL/PGVector

OpenSearch

Pinecone

Job description

• Design and implement end-to-end RAG pipelines integrating proprietary chunking algorithms, embedding models, vector databases, and data retrieval agents


• Build and optimize retrieval systems over large-scale proprietary datasets using advanced embedding techniques


• Develop LLM-based solutions orchestrating retrieval, generation, and ranking for high-quality, context-aware responses


• Investigate and solve challenges in vector search, chunking and indexing strategies, unstructured data retrieval evaluation, and GraphRAG


• Work with Product and Design teams to build ML-based solutions that enhance user experiences and meet business objectives


• Collaborate with ML Operations to automate management of the full ML systems lifecycle from technical design through implementation

Requirements

• Bachelor's degree or higher in Computer Science, Engineering, or a related field


• 3+ years of hands-on industry experience with machine learning, NLP, information retrieval systems, and large-scale text processing


• Experience designing, shipping, and maintaining production systems


• Strong Python programming skills


• Working knowledge of data processing tools and ML frameworks such as PyTorch, Transformers, and HuggingFace


• Experience with LLM orchestration libraries/frameworks such as LangChain and LlamaIndex


• Experience building ML pipelines for data processing, training, inference, maintenance, evaluation, versioning, and experimentation


• Experience with vector databases such as PostgreSQL/PGVector, OpenSearch, and Pinecone


• Understanding of similarity search techniques and vector indexing algorithms


• Effective coding, documentation, collaboration, and communication habits


• Strong problem-solving skills and a proactive approach


• Ability to adapt to a fast-paced and dynamic work environment

Core Competencies

Demonstrates expertise in designing and implementing end-to-end RAG pipelines, leveraging advanced machine learning techniques and frameworks to optimize retrieval systems and enhance user experiences. Proficient in Python programming and familiar with vector databases and LLM orchestration libraries.

Highest-signal resume keywords

• Machine Learning


• Natural Language Processing


• Python Programming


• LLM Orchestration


• Vector Databases

ATS Optimization Keywords

Hard Skills

• Machine Learning


• Natural Language Processing


• Information Retrieval Systems


• Large-Scale Text Processing


• Python Programming


• Data Processing


• ML Pipelines


• Vector Indexing Algorithms


• Chunking Algorithms


• Embedding Models

Soft Skills

• Problem-Solving


• Collaboration


• Communication


• Adaptability


• Proactive Approach

Industry Keywords

• RAG Pipelines


• Data Retrieval


• Similarity Search


• Unstructured Data Retrieval


• ML Systems Lifecycle

Tools & Technologies

• PyTorch


• Transformers


• HuggingFace


• LangChain


• LlamaIndex


• PostgreSQL/PGVector


• OpenSearch


• Pinecone