Applied AI Staff Engineer

Position : Applied AI Staff Engineer

100% Remote
Direct Client Requirement

We are looking for a Staff Applied AI Engineer to build scalable, production-grade backend applications that leverage large and complex datasets. This role is software engineering first, with a strong emphasis on designing reliable systems that ingest, process, and serve data to power modern applications and AI-driven solutions.
You will work closely with product managers, software engineers, data scientists, and ML engineers to build robust backend services, data-intensive applications, and production AI systems. Success in this role requires strong software engineering fundamentals, practical experience working with data throughout its lifecycle, and the ability to design systems that are scalable, maintainable, and reliable in production.

ESSENTIAL JOB FUNCTIONS
Lead technical strategy for AI/LLM systems across multiple products
Architect retrieval, orchestration, agentic, and evaluation systems that run reliably in production
Set the standards for AI safety, evaluation, observability, and responsible rollout in a regulated context
Mentor Junior-level engineers into strong AI engineers; Employ AI Native development skills to multiply the productivity (Claude, etc.)
Lead the frontier: evaluate new models, techniques, and tools, and bring the right ones into the team
Design, develop, and maintain scalable Python applications and backend services.
Build systems that ingest, validate, transform, and manage structured and unstructured data in production environments.
Design data models and storage solutions that support scalable, high-performance applications.
Develop reusable components for data processing, validation, enrichment, and feature generation.
QUALIFICATIONS

Education:
Bachelor's degree in Computer Science, Engineering, Data Science, or a related quantitative field.

Mandatory Skills:
3 years of hands-on experience in machine learning, data science, search
relevance, or ranking systems.
Proven expertise in Python and ML frameworks (MLFlow, TensorFlow, PyTorch, Scikit-
learn, or equivalent).
Strong background in statistical analysis, data exploration, and working with
large-scale datasets.
Experience with feature engineering, data preprocessing, and data

Preferred Skills:
10+ years of software engineering experience, with deep recent time leading production AI/LLM systems
Seasoned hands-on coder; still writes production Python regularly
Seasoned system designer for AI systems at scale retrieval, agents, evaluation, latency, and cost, vector databases/pipelines
Strong experience building and maintaining production-grade backend applications.
Experience designing and developing RESTful APIs and distributed systems.
Strong SQL skills and experience working with relational databases; familiarity with NoSQL databases or modern data storage technologies is a plus.
Solid understanding of data engineering fundamentals, including data quality, validation, transformation, modeling, and efficient storage.
Experience designing systems that process large datasets reliably and efficiently.
Experience with cloud platforms such as Google Cloud Platform (Google Cloud Platform) or AWS.
Experience using Docker, Git, CI/CD pipelines, automated testing frameworks, and modern software engineering best practices.
Core engineering stack
Languages: Python, REST API, Pandas, NumPy
Cloud and infrastructure: AWS Services and/or Google Cloud Platform, Kubernetes, Bedrock
Distributed systems: event-driven architectures, including Kafka
Orchestration Frameworks: LangGraph, LangChain, AirFlow, etc.
Vector Databases like Qdrant

Nice to have Skills:
Experience with Kubernetes and container orchestration.
Familiarity with event-driven architectures and messaging platforms such as Kafka.
Familiarity with ML model deployment and inference pipelines
We're looking for engineers who think like software engineers and are comfortable working with data as a core part of modern applications. You understand how data quality impacts production systems and know how to build reliable, scalable services that make effective use of large datasets.