MANDO TECHNOLOGIES INC
Generative AI Engineer
MANDO TECHNOLOGIES INC, Charlotte, North Carolina, United States, 28245
At Mando Technologies, we specialize in helping organizations unlock the full value of their data. From acquiring and organizing information to analyzing and delivering insights and ultimately integrating that intelligence into day-to-day operations we support the entire Business Intelligence journey from start to finish.
Position Overview The ideal candidate has deep experience in cloud platforms (AWS, Azure, or Google Cloud Platform), hands‑on coding with Python, and a strong understanding of
AI governance and security best practices . You’ll work closely with DevOps, IT Ops, and Architecture teams to design and implement secure, scalable, and production‑ready GenAI solutions.
Key Responsibilities
Develop, deploy, and optimize cloud‑based AI applications using AWS, Azure, or Google Cloud Platform.
Implement and maintain Retrieval‑Augmented Generation (RAG) pipelines.
Integrate
vector databases
(e.g., PostgreSQ, FAISS, Weaviate, Milvus) for knowledge retrieval in GenAI workflows.
Use frameworks like
LangChain ,
OpenAI APIs ,
TensorFlow , and
PyTorch
to create intelligent and interactive systems.
Design and document AI architectures using
Mermaid.js ,
PlantUML , or
Graphviz
for architecture and sequence diagrams.
Build
CI/CD-integrated AI agents
to automate tasks such as code documentation using AI‑powered tools.
Apply enterprise AI governance frameworks, including secure access control, auditing, and data privacy.
Collaborate with IT Ops and DevOps teams to align AI solutions with existing cloud infrastructure and workflows.
Support ongoing maintenance and evolution of production AI systems with monitoring, alerts, and upgrades.
Required Qualifications
Proven experience working with cloud‑based AI services (AWS, Azure, or Google Cloud Platform).
Strong hands‑on expertise with Retrieval‑Augmented Generation (RAG) implementations.
Proficiency in Python and at least one of: TensorFlow, PyTorch, LangChain, OpenAI APIs.
Practical experience integrating and managing vector databases (e.g., PostgreSQL, FAISS, Weaviate, Milvus).
Solid understanding of CI/CD pipelines, DevOps practices, and automation tooling.
Experience developing GenAI solutions tailored for IT operations and DevOps environments.
Working knowledge of diagramming tools like Mermaid.js, PlantUML, or Graphviz for design documentation.
Familiarity with AI‑powered documentation tools to automate code comments and generate UML diagrams.
Strong understanding of enterprise security practices and AI governance frameworks.
Nice to Have
Background in AI observability, model performance monitoring, or prompt engineering.
Experience integrating Generative AI into enterprise platforms or internal tools.
Certification in AWS/Azure cloud architecture or AI/ML specialties.
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Position Overview The ideal candidate has deep experience in cloud platforms (AWS, Azure, or Google Cloud Platform), hands‑on coding with Python, and a strong understanding of
AI governance and security best practices . You’ll work closely with DevOps, IT Ops, and Architecture teams to design and implement secure, scalable, and production‑ready GenAI solutions.
Key Responsibilities
Develop, deploy, and optimize cloud‑based AI applications using AWS, Azure, or Google Cloud Platform.
Implement and maintain Retrieval‑Augmented Generation (RAG) pipelines.
Integrate
vector databases
(e.g., PostgreSQ, FAISS, Weaviate, Milvus) for knowledge retrieval in GenAI workflows.
Use frameworks like
LangChain ,
OpenAI APIs ,
TensorFlow , and
PyTorch
to create intelligent and interactive systems.
Design and document AI architectures using
Mermaid.js ,
PlantUML , or
Graphviz
for architecture and sequence diagrams.
Build
CI/CD-integrated AI agents
to automate tasks such as code documentation using AI‑powered tools.
Apply enterprise AI governance frameworks, including secure access control, auditing, and data privacy.
Collaborate with IT Ops and DevOps teams to align AI solutions with existing cloud infrastructure and workflows.
Support ongoing maintenance and evolution of production AI systems with monitoring, alerts, and upgrades.
Required Qualifications
Proven experience working with cloud‑based AI services (AWS, Azure, or Google Cloud Platform).
Strong hands‑on expertise with Retrieval‑Augmented Generation (RAG) implementations.
Proficiency in Python and at least one of: TensorFlow, PyTorch, LangChain, OpenAI APIs.
Practical experience integrating and managing vector databases (e.g., PostgreSQL, FAISS, Weaviate, Milvus).
Solid understanding of CI/CD pipelines, DevOps practices, and automation tooling.
Experience developing GenAI solutions tailored for IT operations and DevOps environments.
Working knowledge of diagramming tools like Mermaid.js, PlantUML, or Graphviz for design documentation.
Familiarity with AI‑powered documentation tools to automate code comments and generate UML diagrams.
Strong understanding of enterprise security practices and AI governance frameworks.
Nice to Have
Background in AI observability, model performance monitoring, or prompt engineering.
Experience integrating Generative AI into enterprise platforms or internal tools.
Certification in AWS/Azure cloud architecture or AI/ML specialties.
#J-18808-Ljbffr