Gen AI Engineer
PapigenAbout the role
Role Summary
We are seeking an experienced GenAI Engineer to design, build, and operationalize enterprise-grade AI solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, and intelligent orchestration frameworks. The ideal candidate will play a key role in developing scalable AI platforms, integrating enterprise knowledge systems, implementing Responsible AI controls, and enabling AI-powered business solutions across cloud environments.
Scope Of Work & Key Responsibilities
Architect and implement end-to-end GenAI solutions using LLMs, RAG frameworks, embeddings, vector search, and enterprise knowledge sources.
Design and build AI agents, multi-agent systems, and orchestration workflows for enterprise use cases.
Develop document ingestion pipelines including OCR, metadata extraction, enrichment, and content processing.
Implement citation validation, reference verification, and grounding mechanisms to improve AI response accuracy.
Design APIs, SDKs, and reusable integration frameworks for AI-enabled applications.
Build orchestration pipelines using platforms such as Workato, AWS services, DigitalNet, or equivalent automation solutions.
Deploy and manage AI workloads on Azure OpenAI, Azure AI Search, Azure Machine Learning, Azure Kubernetes Service (AKS), and related platforms.
Integrate GenAI services with enterprise applications including SharePoint and other content management systems.
Implement Responsible AI controls covering governance, monitoring, explainability, security, compliance, and risk management.
Conduct technical evaluations, proof-of-concepts (POCs), platform assessments, and architecture reviews.
Develop technical documentation, training materials, operational runbooks, and knowledge transfer assets.
Collaborate with business, engineering, security, and architecture teams to deliver scalable AI solutions.
Support deployment, monitoring, troubleshooting, performance tuning, and production operations of AI platforms.
Required Skills & Experience
Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related discipline.
Strong experience designing and implementing enterprise AI and GenAI solutions.
Hands-on experience building RAG pipelines including chunking strategies, embeddings, retrieval workflows, and grounding mechanisms.
Experience working with vector databases and search platforms such as:
Azure AI Search
Redis
PostgreSQL
Similar vector-enabled databases
Expertise with LLM platforms including:
Azure OpenAI
Claude
Gemini
Similar GenAI platforms
Strong knowledge of prompt engineering, AI evaluation frameworks, hallucination detection, and response validation techniques.
Experience with AI orchestration tools such as Workato, AWS AI services, DigitalNet, or similar workflow platforms.
Understanding of Responsible AI frameworks including Credo AI, NIST AI RMF, governance controls, and compliance requirements.
Experience with document processing, OCR technologies, metadata enrichment, and enterprise content management.
Strong knowledge of Azure and AWS cloud platforms.
Experience with CI/CD pipelines, DevSecOps practices, Kubernetes, and cloud-native deployment models.
Strong analytical, troubleshooting, and problem-solving skills.
Excellent communication, stakeholder management, and documentation abilities.
Required Certifications
Microsoft Certified: Azure AI Fundamentals (AI-900)
Microsoft Certified: Azure Data Fundamentals (DP-900)
Responsible AI Certification(s)
AWS Machine Learning Specialty
TensorFlow Developer Certification
Kubernetes CKA or CKAD Certification
SAFe ASE (Architecting for SAFe)
Preferred Certifications
Microsoft Certified: Azure AI Engineer Associate (AI-102)
Microsoft Certified: Azure Data Scientist Associate (DP-100)
Microsoft Certified: Azure Solutions Architect Expert (AZ-305)
Microsoft Certified: Azure Developer Associate (AZ-204)
Skills: devsecops,postgresql,langchain,azure openai,rag,gen ai,azure ai search,fastapi,ai-900,azure sql,az-305,azure kubernetes,proof of concept,azure devops,semantic kernel,dp-100,azure,redis,llm,autogen,dp-900,az-204,responsible ai frameworks,kubernetes
Responsibilities
- Architect and implement end-to-end GenAI solutions using LLMs and RAG frameworks
- Design and build AI agents and orchestration workflows for enterprise use cases
- Develop document ingestion pipelines including OCR and metadata extraction
- Implement citation validation and grounding mechanisms to improve AI response accuracy
- Design APIs, SDKs, and reusable integration frameworks for AI-enabled applications
- Build orchestration pipelines using platforms such as Workato and AWS services
- Deploy and manage AI workloads on Azure and related platforms
- Integrate GenAI services with enterprise applications
Qualifications
- Bachelor’s degree in Computer Science, Artificial Intelligence, or related discipline
- Strong experience designing and implementing enterprise AI solutions
- Hands-on experience building RAG pipelines and working with vector databases
- Expertise with LLM platforms and AI orchestration tools
- Understanding of Responsible AI frameworks and compliance requirements
- Experience with document processing and cloud platforms
- Strong analytical and problem-solving skills
- Excellent communication and stakeholder management abilities
Skills mentioned
About Papigen
Papigen is a world-class technology services business that incorporates industry insights and experience to deliver the digital solutions. As a Global IT Services and Solutions Company, Papigen can assist in bringing Technology Transformation and Modernization across Business Functions and Enterprises. Improving existing technology processes and platforms, Enterprise Digital Integration and Enablement, User Engagement through better Visualization and User Interface, Enterprise Mobility, Extensive Capability in Dynamic Technology areas such as Big Data, Java, React, MEAN/MERN, Cloud, DevOps and much more. We are fast growing company looking to deliver the best quality possible and achieve clients need by offering a unique mix of software products, consulting services, data science capabilities, and technology expertise. We work with our customers on Time and Material Engagements, Staff Augmentation, as well as SoW led Managed Services Models. To learn more, visit www.papigen.com.