Job Summary
We are seeking a highly experienced Staff Applied AI Engineer to design and build scalable, production-grade backend applications powered by large and complex datasets, machine learning, and modern AI/LLM technologies.
This is a software-engineering-first AI role focused on building reliable systems that ingest, process, transform, retrieve, and serve data for modern applications and AI-driven solutions.
The ideal candidate will have strong hands-on experience with Python, backend engineering, machine learning, data-intensive applications, retrieval systems, LLM/agentic architectures, and distributed systems. You will collaborate closely with Product Managers, Software Engineers, Data Scientists, and ML Engineers to deliver scalable and maintainable AI systems in production.
Key Responsibilities
- Lead the technical strategy and architecture for AI/LLM systems across multiple products.
- Architect and implement reliable retrieval, orchestration, agentic AI, and evaluation systems for production environments.
- Establish engineering standards for AI safety, evaluation, observability, reliability, and responsible deployment.
- Mentor junior and mid-level engineers and help develop strong AI engineering practices.
- Apply AI-native development practices and tools such as Claude and other AI-assisted development technologies to improve engineering productivity.
- Evaluate emerging AI models, techniques, frameworks, and tools and determine their applicability to production systems.
- Design, develop, test, and maintain scalable Python applications and backend services.
- Build systems for ingesting, validating, transforming, enriching, and managing structured and unstructured data.
- Design scalable data models and storage architectures for high-performance applications.
- Develop reusable components for data processing, validation, enrichment, feature generation, and AI/ML workflows.
- Design and develop RESTful APIs and distributed backend services.
- Build systems capable of processing large datasets reliably and efficiently.
- Collaborate with product, engineering, data science, and ML teams to deliver production-ready AI solutions.
- Ensure systems meet requirements for performance, scalability, reliability, observability, and cost efficiency.
Required Qualifications
Education
Bachelor's degree in Computer Science, Engineering, Data Science, or a related quantitative field.
Mandatory Technical Skills
- Minimum 3 years of hands-on experience in machine learning, data science, search relevance, ranking systems, or a closely related field.
- Strong hands-on expertise in Python.
- Experience with ML frameworks such as:
- MLflow
- TensorFlow
- PyTorch
- Scikit-learn
- Or equivalent machine learning frameworks
- Strong understanding of statistical analysis and data exploration.
- Experience working with large-scale datasets.
- Strong experience with:
- Feature engineering
- Data preprocessing
- Data transformation
- Data quality and validation
- Data modeling
Preferred Qualifications
- 10+ years of software engineering experience, with recent experience leading production AI/LLM systems.
- Strong hands-on coding ability with a willingness to write production-quality Python regularly.
- Experience designing AI systems at scale, including:
- Retrieval systems
- Agentic AI
- LLM orchestration
- Evaluation frameworks
- AI observability
- Latency optimization
- Cost optimization
- Vector databases and pipelines
- Strong experience developing and maintaining production-grade backend applications.
- Experience designing RESTful APIs and distributed systems.
- Strong SQL skills and experience with relational databases.
- Experience with NoSQL databases and modern data storage technologies.
- Strong understanding of data engineering fundamentals, including data quality, validation, transformation, modeling, and efficient storage.
- Experience designing systems that process large volumes of data reliably and efficiently.
- Experience with cloud platforms such as AWS and/or Google Cloud Platform.
- Experience with:
- Docker
- Git
- CI/CD
- Automated testing
- Modern software engineering practices
Core Technology Stack
Programming & Data
Python
REST APIs
Pandas
NumPy
SQL
AI / Machine Learning
LLMs
Machine Learning
MLflow
PyTorch / TensorFlow / Scikit-learn
Retrieval-Augmented Generation (RAG)
Agentic AI
AI evaluation and observability
Cloud & Infrastructure
AWS and/or Google Cloud Platform
AWS Bedrock
Kubernetes
Docker
Distributed Systems
Event-driven architectures
Apache Kafka
Distributed backend services
AI / Workflow Orchestration
LangGraph
LangChain
Airflow
Similar orchestration frameworks
Vector & Data Technologies
Qdrant or other vector databases
Relational databases
NoSQL / modern data storage technologies
Nice-to-Have Skills
- Hands-on experience with Kubernetes and container orchestration.
- Experience with event-driven architectures and messaging platforms such as Kafka.
- Experience with ML model deployment and production inference pipelines.
- Experience building and operating AI/LLM applications in production.
- Experience with AI safety, evaluation, monitoring, and responsible AI practices.
- Experience optimizing AI systems for production latency, scalability, reliability, and cost.
Ideal Candidate Profile
The ideal candidate is a Staff-level software engineer with strong Applied AI expertise who combines deep software engineering fundamentals with practical experience building production AI systems.
You should be comfortable working across the complete lifecycle of data and AI applications—from data ingestion and preprocessing through retrieval, model inference, orchestration, evaluation, and production operations.
Strong candidates will demonstrate the ability to:
Think like a software architect while remaining hands-on with code.
Build scalable Python backend services.
Design reliable AI/LLM systems for production.
Work effectively with large and complex datasets.
Understand how data quality affects AI system performance.
Design distributed and event-driven architectures.
Collaborate effectively with product, engineering, data science, and ML teams.
Evaluate emerging AI technologies and make practical technology decisions.
Mentor engineers and establish strong engineering standards.