Envision Technology Solutions
AI ML Architect
Job description
AI/ML Sr. Architect
About the Role
We are seeking a highly experienced AI / ML Engineer to design, develop, and deploy machine‑learning solutions that power key analytics and intelligent automation across our fintech ecosystem. This role focuses on delivering production‑grade ML systems—spanning classical ML, deep learning, and LLM‑based applications—while ensuring scalability, reliability, and regulatory compliance. The engineer will own end‑to‑end model development, from data preparation through deployment and monitoring, and will work closely with engineering, product, and data teams to implement impactful AI capabilities.
Key Responsibilities
• Build, train, and evaluate ML and deep learning models for classification, prediction, anomaly detection, and NLP use cases.
• Implement scalable ML pipelines for data processing, feature engineering, and inference.
• Develop and integrate LLM‑based capabilities including embeddings, RAG workflows, and fine‑tuned models.
• Deploy models to production using containerized and cloud‑native infrastructures (Docker, Kubernetes, Azure/AWS).
• Implement MLOps practices including CI/CD integration, experiment tracking, model registries, and monitoring.
• Ensure high‑quality data pipelines and automate preprocessing for structured and unstructured data.
• Apply model explainability (SHAP, LIME) and Responsible AI principles to ensure transparency and safe use.
Responsibilities
- We are seeking a highly experienced AI / ML Engineer to design, develop, and deploy machine‑learning solutions that power key analytics and intelligent automation across our fintech ecosystem
- This role focuses on delivering production‑grade ML systems—spanning classical ML, deep learning, and LLM‑based applications—while ensuring scalability, reliability, and regulatory compliance
- The engineer will own end‑to‑end model development, from data preparation through deployment and monitoring, and will work closely with engineering, product, and data teams to implement impactful AI capabilities
- Build, train, and evaluate ML and deep learning models for classification, prediction, anomaly detection, and NLP use cases
- Implement scalable ML pipelines for data processing, feature engineering, and inference
- Develop and integrate LLM‑based capabilities including embeddings, RAG workflows, and fine‑tuned models
- Deploy models to production using containerized and cloud‑native infrastructures (Docker, Kubernetes, Azure/AWS)
- Implement MLOps practices including CI/CD integration, experiment tracking, model registries, and monitoring
- Ensure high‑quality data pipelines and automate preprocessing for structured and unstructured data
- Apply model explainability (SHAP, LIME) and Responsible AI principles to ensure transparency and safe use
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