Solutions Architect - AI & Platforms

Skills & Requirements

• Minimum 10+ years of experience in software engineering, system architecture, or platform engineering and minimum 1+ years of experience in AI solutioning.
• Proven hands-on experience in developing and productionizing AI based solution.
• Strong foundation in Machine Learning, LLMs, and modern AI architectures.
• Solid experience in system design and architecture trade-offs (performance, scalability, cost, reliability).
• Hands-on experience with microservices architecture, API design, and event-driven systems.
• Familiarity with data architecture and data pipelines using tools such as Apache Spark, Airflow, Prefect, Dagster, or Kafka-based pipelines will be added advantage.
• Familiarity with model serving and inference frameworks such as Ollama/TensorFlow Serving/ Triton Inference Server/vLLM.
• Familiarity with MLOps / LLMOps practices, including model deployment, monitoring, and lifecycle management, including tools such as MLflow, Kubeflow, Weights & Biases.
• Strong programming experience in Python (preferred) or Java, Node.js, or C#.
• Strong foundation in containerization and exposure to deployment of containerized application on production environment
• Experience with vector databases and semantic search systems such as Pinecone, Weaviate, Milvus, Qdrant, Chroma, or Elasticsearch/OpenSearch will be an added advantage.
• Strong understanding of DevOps practices, CI/CD pipelines, and code review workflows using tools such as Git, GitHub/GitLab, Jenkins, GitHub Actions, or ArgoCD.
• Any exposure to AI evaluation frameworks, guardrails, and model monitoring will be added advantage