ML Engineer
e-IT Professionals Corp.
San Diego, CA, United States
1 day ago
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Role details
Contract type
Contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours
Job source
Tech stack
Java (Programming Language)
JavaScript (Programming Language)
Artificial Intelligence
Artificial Neural Networks
Cluster Analysis
Data Structures
Data Stores
Python (Programming Language)
NoSQL
NumPy
Performance Tuning
Rapid Prototyping Process
+12 more
Tensorflow
Pytorch
Retrieval-Augmented Generation
Large Language Models
Prompt Engineering
Backend
Agentic-AI
Pandas
Kubernetes
Information Technology
Machine Learning Operations
Microservices
Job description
- Design and own shared AI/ML components, frameworks, and pipelines - spanning model training, evaluation, and fine-tuning - used across multiple product teams
- Apply ML fundamentals and LLM techniques (prompting, fine-tuning, RAG) to solve concrete customer problems, then evaluate model performance in production
- Prototype and build agent builder frameworks and embedded AI experiences, taking cutting-edge agentic AI concepts from early exploration to product-ready implementations
- Drive rapid prototyping and experimentation to move from proof-of-concept to high-accuracy, performant systems
- Architect and build full-stack, AI-native applications end-to-end, from backend services to production LLM integrations
Requirements
- BS or higher in Computer Science or equivalent practical experience
- 5+ years building production AI/ML systems; prior experience leading an engineering effort a plus
- Strong CS fundamentals - data structures, algorithms, system design - plus solid ML fundamentals (classification, regression, clustering, neural networks)
- Proficiency in Python and standard ML frameworks (PyTorch, TensorFlow, Pandas/NumPy)
- Practical experience with LLMs: prompt engineering, fine-tuning, and frameworks like LangChain
- Experience building or operating agentic systems, agent frameworks, or long-term memory/context architectures for AI applications
- Familiarity designing evaluation frameworks or offline/online eval pipelines for measuring model and agent quality
- Track record of launching AI integrations in production and evaluating their real-world impact
- Strong cross-functional collaboration skills, partnering effectively with data scientists, product managers, and engineers across global teams
- Proficiency in one or more full-stack languages (JavaScript, Java) and experience designing scalable services - microservices, relational/NoSQL data stores, Kubernetes
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