AI/ML Engineer
Diverse Lynx LLC
Hartford, United States of America
2 days ago
Role details
Contract type
Permanent contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
English Experience level
Senior Compensation
$ 101KJob location
Hartford, United States of America
Tech stack
Clean Code Principles
API
Artificial Intelligence
Continuous Integration
Information Engineering
Python
Machine Learning
Performance Tuning
Rapid Prototyping Process
TensorFlow
Software Engineering
Tokenization
PyTorch
Flask
Large Language Models
Prompt Engineering
Deep Learning
Model Validation
FastAPI
HuggingFace
Streamlit Framework
Data Pipelines
Job description
We are seeking an AI/ML Engineer with hands-on experience building, fine-tuning, and deploying LLM-based solutions. You will work on NLP/GenAI use cases such as classification, summarization, and retrieval-augmented generation (RAG), partnering with product and engineering teams to deliver scalable, secure, and measurable outcomes. Responsibilities
- Design, build, and fine-tune NLP/LLM solutions for business use cases (e.g., classification, summarization, Q&A).
- Develop efficient, well-documented Python code for training, inference, and evaluation pipelines.
- Build RAG applications using embeddings, vector databases, and prompt engineering techniques.
- Integrate LLM applications into services/APIs and ensure performance, reliability, and scalability.
- Establish model evaluation, monitoring, and governance practices (quality, safety, bias, drift).
- Collaborate with data engineering and platform teams on data pipelines, deployments, and CI/CD., Johnson Controls, a global leader in thermal management, mission-critical building systems, energy efficiency, and decarbonization, helps customers use energy more productively, re…
- 2 days ago
Requirements
- 6+ years of overall experience in software development focusing on AI/ML engineering.
- 2+ years of hands-on experience with deep learning for NLP/GenAI.
- Strong Python proficiency, including writing production-quality, testable, maintainable code.
- Experience with deep learning frameworks and libraries: PyTorch or TensorFlow; Hugging Face Transformers.
- Solid understanding of deep learning architectures and modern NLP/LLM concepts (tokenization, attention/transformers, fine-tuning approaches).
- Experience building rapid prototypes and APIs using FastAPI/Flask and/or Streamlit.