Offer summary

Qualifications:

Strong programming skills in Python and experience with AI/ML libraries like PyTorch or TensorFlow., Deep knowledge of Azure AI services (Azure OpenAI, Azure ML) and AWS AI/ML services (SageMaker, Bedrock)., Experience with cloud architecture, DevOps, and Infrastructure as Code tools such as Terraform or CloudFormation., Familiarity with LLM orchestration frameworks like LangChain and Semantic Kernel..

Key responsibilities:

Design and architect GenAI solutions on Azure and AWS platforms.

Develop and fine-tune large language models using frameworks like Hugging Face Transformers.

Deploy and monitor GenAI models in production environments, ensuring performance and cost-effectiveness.

Collaborate with cross-functional teams and document architecture and operational procedures.

Job description

This is a remote position.

Job Summary:

We are looking for a highly skilled Generative AI Developer with deep expertise in designing, developing, and deploying GenAI solutions on Microsoft Azure and AWS. The ideal candidate will have hands-on experience in production-grade AI/ML systems, a strong understanding of LLM architectures, and the ability to architect scalable, secure, and cost-effective GenAI solutions across both cloud platforms.

Key Responsibilities:

1. Solution Architecture & Design

Design and architect GenAI solutions using Azure AI (Azure OpenAI, Azure ML) and/or AWS AI/ML services (Bedrock, SageMaker, Comprehend, Lex).
Implement cloud-native architectures for LLM-based applications, including multi-cloud or hybrid deployments.
Define and manage MLOps pipelines for model training, deployment, and monitoring using Azure ML Pipelines or AWS SageMaker Pipelines.

2. Development & Implementation

Develop and fine-tune LLMs using frameworks like Hugging Face Transformers, LangChain, Semantic Kernel, or AWS LangChain SDK.
Implement prompt engineering, retrieval-augmented generation (RAG), and vector search using Azure AI Search, Amazon Kendra, or OpenSearch.
Build APIs and microservices to expose GenAI capabilities using Azure Functions, AWS Lambda, or containerized services.

3. Production Deployment

Deploy GenAI models using Azure Kubernetes Service (AKS), Azure Container Apps, Amazon EKS, or Fargate.
Monitor and optimize model performance, latency, and cost in production environments using Azure Monitor, AWS CloudWatch, and custom telemetry.
Implement observability, logging, and alerting for AI workloads across both platforms.

4. Collaboration & Documentation

Collaborate with cross-functional teams including data scientists, DevOps, and product managers.
Document architecture, design decisions, and operational procedures.
Provide mentorship and conduct code reviews for junior developers.

Required Skills & Qualifications:

Technical Skills:

Strong programming skills in Python (preferred), with experience in AI/ML libraries (e.g., PyTorch, TensorFlow).
Deep knowledge of Azure AI services (Azure OpenAI, Azure ML, Cognitive Services) and/or AWS AI/ML services (Bedrock, SageMaker, Comprehend, Rekognition).
Experience with cloud architecture, DevOps, and Infrastructure as Code (Terraform, Bicep, AWS CloudFormation).
Familiarity with LLM orchestration frameworks: LangChain, Semantic Kernel, Prompt Flow.
Experience with vector databases: Azure AI Search, Amazon Kendra, Pinecone, FAISS, Weaviate.

Soft Skills:

Strong analytical and problem-solving skills.
Excellent communication and collaboration abilities.
Ability to work in a fast-paced, agile environment.

Preferred Qualifications:

Microsoft Certified: Azure AI Engineer Associate or Azure Solutions Architect Expert.
AWS Certified Machine Learning – Specialty or AWS Solutions Architect – Professional.
Experience with multi-modal GenAI (text, image, audio).
Knowledge of responsible AI, data privacy, and model governance.