[Hiring] AI/ML Solution Architect @DMS Vision

Role Description

We are seeking a highly skilled AI/ML Solution Architect with strong experience in designing and implementing scalable AI/ML and Generative AI solutions. The ideal candidate will have hands-on expertise in LLM architectures, MLOps, and Azure-based AI services, along with the ability to lead end-to-end solution design for enterprise-grade applications.

• Design and architect AI/ML and Generative AI solutions for production environments
• Develop and implement LLM-based applications using modern GenAI techniques
• Build and manage end-to-end ML lifecycle (MLOps) including training, deployment, monitoring, and retraining
• Implement RAG (Retrieval-Augmented Generation) solutions using vector databases
• Collaborate with cross-functional teams to define architecture and technical solutions
• Ensure scalability, performance, and reliability of AI systems
• Apply security, governance, and responsible AI practices
• Provide technical leadership and communicate effectively with stakeholders

Qualifications

• Strong experience in AI/ML solution architecture
• Hands-on experience with:
  • Machine Learning & Deep Learning
  • Generative AI / LLMs
• Expertise in GenAI concepts:
  • Prompt Engineering
  • Vector Databases
  • RAG architectures
• Experience with MLOps and model lifecycle management
• Strong knowledge of Azure Cloud (Azure ML, Azure OpenAI, etc.)
• Proficiency in:
  • Python (mandatory)
  • C# and .NET Core
• Experience with AI/ML frameworks:
  • TensorFlow
  • PyTorch
  • Scikit-learn
  • Hugging Face
  • LangChain
• Strong understanding of:
  • Microservices architecture
  • APIs
  • Event-driven systems
• Knowledge of security, compliance, and AI governance
• Excellent communication and leadership skills

Preferred Skills

• Experience with data architecture (data lakes, feature stores, pipelines)
• Familiarity with DevOps tools (Docker, Kubernetes, CI/CD)
• Exposure to Big Data technologies (Spark, Kafka, Snowflake, Databricks)
• Experience in performance tuning and cost optimization
• Understanding of AI ethics and regulatory requirements