< data-section-id="1y72hpo" data-start="0" data-end="64">Job Title: Generative AI Engineer (Agentic AI / LLM Solutions)
Location: Phoenix, AZ / New York City, NY (Hybrid/Onsite)
Duration: 12+ Months Contract
Interview Mode: Video Interview
Position Overview
We are seeking a highly skilled and innovative Generative AI Engineer to join our growing AI and Digital Transformation team. This role will focus on designing, developing, deploying, and optimizing enterprise-scale Generative AI and Agentic AI solutions that drive operational excellence, enhance customer experiences, and support business decision-making.
The ideal candidate will possess strong expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI frameworks, distributed systems, and cloud-native application development. The candidate will be responsible for building production-grade AI systems capable of autonomous reasoning, tool utilization, contextual memory management, and enterprise workflow orchestration while ensuring compliance, security, explainability, and responsible AI standards.
This position offers an opportunity to work on cutting-edge AI initiatives that will directly impact business operations and customer engagement across the organization.
Key Responsibilities
Generative AI & Agentic AI Development
- Design, architect, develop, and deploy scalable Generative AI and Agentic AI solutions for enterprise applications.
- Build intelligent autonomous agents capable of contextual reasoning, decision-making, workflow orchestration, and tool integration.
- Develop multi-agent architectures leveraging advanced orchestration frameworks and memory management techniques.
- Implement AI-powered solutions that improve operational efficiency, automate business processes, and enhance customer interactions.
LLM & RAG System Engineering
- Design and implement production-grade LLM-powered applications and services.
- Develop and optimize Retrieval-Augmented Generation (RAG) architectures using vector databases and enterprise knowledge sources.
- Create robust prompt engineering, context engineering, and evaluation frameworks to improve model performance and reliability.
- Manage the complete LLM lifecycle, including model selection, fine-tuning, deployment, monitoring, and continuous improvement.
- Implement mechanisms for hallucination reduction, response validation, and AI quality assurance.
Data Engineering & Pipeline Development
- Design and build large-scale data pipelines supporting AI and machine learning workloads.
- Integrate structured and unstructured enterprise data sources into AI systems.
- Develop scalable ETL/ELT processes and data ingestion frameworks.
- Collaborate with data engineering teams to operationalize AI-ready data products.
Cloud & Platform Engineering
- Build cloud-native AI applications using modern software engineering principles.
- Develop and maintain backend APIs and microservices using FastAPI, Flask, and Python.
- Deploy and manage AI workloads using Docker, Kubernetes, and Google Cloud Platform services.
- Implement CI/CD pipelines for AI application deployment and lifecycle management.
- Ensure high availability, scalability, and resilience of deployed AI solutions.
AI Governance & Responsible AI
- Establish AI monitoring, evaluation, and governance frameworks.
- Implement explainability, observability, auditability, and compliance controls within AI systems.
- Ensure adherence to enterprise security standards, regulatory requirements, and responsible AI practices.
- Monitor model performance, drift, usage patterns, and business impact metrics.
Cross-Functional Collaboration
- Partner with product managers, business stakeholders, architects, and engineering teams to identify and prioritize AI opportunities.
- Translate complex business requirements into scalable technical solutions.
- Provide technical leadership and guidance on AI architecture, implementation strategies, and best practices.
- Participate in architecture reviews, code reviews, and technical design discussions.
Support & Continuous Improvement
- Provide production support and troubleshooting for deployed AI platforms and services.
- Continuously evaluate emerging technologies, frameworks, and industry trends within Generative AI and Agentic AI ecosystems.
- Drive innovation through experimentation, prototyping, and proof-of-concept development.
Required Qualifications
- Bachelor''s or Master''s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related technical field.
- Minimum 6+ years of professional experience in Software Engineering, Machine Learning, Artificial Intelligence, or related domains.
- Proven experience developing and deploying production-grade Generative AI solutions.
- Strong expertise in Agentic AI architectures, including:
- Agent orchestration
- Tool usage and integrations
- Context management
- Memory/state management
- Multi-agent systems
- Evaluation frameworks
- Extensive experience implementing:
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Vector databases
- Semantic search solutions
- Knowledge retrieval systems
- Strong programming proficiency in Python.
- Experience developing backend services and APIs using:
- Hands-on experience with:
- Docker
- Kubernetes
- CI/CD pipelines
- Cloud-native development
- Experience with Google Cloud Platform (Google Cloud Platform) services and infrastructure.
- Strong understanding of SQL and database design principles.
- Experience working with:
- Relational Databases
- NoSQL Databases
- BigQuery
- Knowledge of distributed systems and event-driven architectures.
- Experience building scalable data processing and analytics solutions.
- Ability to review and understand code across multiple programming languages including:
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