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AI Data Engineer--LLMs / Agentic Systems
Pfizer —Cambridge, MA
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AI Engineer

Better Direct -
Tempe, AZ

Quick Apply

Job Details

Full-time
$60,000 - $80,000 a year

Qualifications

AI models
Index optimization
TensorFlow
Data transformation pipeline development
NumPy
Generative models
AI platforms (beyond public GPTs)
Computational framework
Pandas
Machine intelligence
GitHub
Neural networks
Metadata
Benchmarking
Machine learning libraries
Model evaluation
Machine learning frameworks

Full Job Description

About the Role

We are looking for a motivated AI Engineer who is passionate about large language models (LLMs), machine learning, and applied AI systems. This position is focused on building real-world AI systems, not just experimenting with models.

You will work on projects involving open-source LLMs, retrieval-augmented generation (RAG) pipelines, vector databases, and AI-powered document processing systems. The goal is to build scalable AI workflows that solve practical problems such as knowledge retrieval, document analysis, and AI-assisted automation.

This role is ideal for engineers who want hands-on experience deploying AI systems used in production-like environments.

Responsibilities

AI & Machine Learning Development

Build and experiment with open-source LLM and SLM pipelines.
Design and implement Retrieval Augmented Generation (RAG) systems.
Develop AI pipelines capable of processing documents, PDFs, and structured data.
Work with embedding models and vector search systems.
Implement prompt engineering, model evaluation, and response optimization.
Assist with fine-tuning or adapting open-source models when necessary.

Data Processing & AI Pipelines

Build ingestion pipelines for PDFs, documents, and datasets.
Implement document chunking, embedding generation, and indexing strategies.
Work with vector databases to support semantic search and retrieval.
Optimize pipelines for latency, scalability, and cost efficiency.

Research & Experimentation

Evaluate different open-source models and architectures.
Compare embedding models and retrieval methods.
Test improvements in RAG performance and hallucination reduction.
Explore emerging techniques in Vision Language Models (VLMs).

Collaboration

Work with engineers to integrate AI components into applications.
Document experiments and technical findings.
Participate in weekly discussions on AI architecture decisions and improvements.

Required Skills & Knowledge

Core AI Knowledge

Strong understanding of:
Machine Learning
Neural Networks
Deep Learning fundamentals
Familiarity with:
Large Language Models (LLMs)
Small Language Models (SLMs)

Programming

Strong proficiency in Python
Experience with AI/ML frameworks such as:
PyTorch
TensorFlow
Hugging Face Transformers

AI Application Frameworks

Experience with:

LangChain or similar orchestration frameworks
Prompt engineering and AI workflow building

Vector Databases

Conceptual and practical understanding of any vector databases such as:

Pinecone
ChromaDB
Milvus
Qdrant
FAISS
Weaviate

Understanding of:

embeddings
similarity search
indexing strategies
metadata filtering

RAG Systems

Ability to design or understand:

Retrieval pipelines
Document chunking strategies
Embedding pipelines
Hybrid search
Context window optimization
RAG evaluation methods

Data Processing

Experience with:

PDF extraction
Document parsing pipelines
Data preprocessing

Bonus Knowledge

Vision Language Models (VLMs)
Multimodal AI systems
Distributed AI inference
GPU inference optimization

Preferred Project Experience

Candidates should have completed at least 1–3 hands-on AI projects, such as:

Example Project 1 – RAG Knowledge Assistant

Built a chatbot that answers questions from internal documentation.
Implemented document ingestion, chunking, embedding generation, and vector search.
Used LangChain + vector database + open-source LLM.

Example Project 2 – Document AI System

Created a system that extracts structured information from PDFs.
Built pipelines for PDF parsing → embeddings → AI summarization.

Example Project 3 – AI Research Experiment

Compared multiple embedding models and evaluated search accuracy.
Benchmarked RAG response quality and hallucination rates.

Example Project 4 – LLM Application

Built a real-world tool using open-source models (e.g., summarizer, Q&A system, coding assistant).

Real-World Experience

Candidates may also have experience such as:

Contributing to open-source AI projects
Participating in AI hackathons
Research experience in machine learning or NLP
Building production-style AI APIs
Deploying models using Docker or cloud platforms
Working with LLM inference servers (vLLM, TGI, Ollama, etc.)

Tools & Technologies (Exposure Preferred)

AI / ML

PyTorch
Hugging Face
Transformers
Sentence Transformers

LLM Tools

LangChain
LlamaIndex
Open-source LLMs (LLaMA, Mistral, etc.)

Vector Databases

Pinecone
ChromaDB
Milvus
Qdrant
FAISS
Weaviate

Data Tools

Pandas
NumPy

Deployment

Docker
REST APIs / FastAPI
Basic cloud exposure (AWS/GCP/Azure)

What You Will Gain

Hands-on experience building real AI systems
Exposure to modern LLM architecture and AI infrastructure
Experience with RAG pipelines and vector databases
Mentorship from engineers working in applied AI
Opportunity to contribute to real production-style AI tools

Candidate Profile

We are looking for someone who:

Is curious and loves experimenting with AI systems
Enjoys solving practical engineering problems
Can quickly learn new frameworks and models
Is comfortable reading research papers and technical documentation
Has strong problem-solving and debugging skills

Application Requirements

Please include:

Resume
GitHub profile (required)
Links to AI/ML projects
Brief description of a RAG or LLM system you have built

Pay: $60,000.00 - $80,000.00 per year

Work Location: In person

Quick Apply

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