- Company Name
- Enterprise Solutions Inc.
- Job Title
- AI & ML Engineer/Architect
- Job Description
-
**Job title:** AI & ML Engineer/Architect
**Role Summary**
Design, develop, and deploy scalable, production‑grade AI/ML solutions on Microsoft Azure and Amazon Web Services (AWS). Lead end‑to‑end ML pipelines, integrate with data lakes/warehouses, implement MLOps practices, and ensure security, compliance, and responsible AI standards.
**Expectations**
- Deliver robust, high‑performance models that meet business objectives.
- Maintain CI/CD for ML lifecycle, monitor and retrain models proactively.
- Collaborate with data, dev‑ops, and product teams to align technical solutions with business goals.
**Key Responsibilities**
- Architect AI/ML workflows using Azure ML, Azure Cognitive Services, Azure Databricks, AWS SageMaker, Rekognition, Comprehend, and related services.
- Design supervised, unsupervised, and deep‑learning models; select algorithms and frameworks, perform feature engineering, exploratory analysis, and model evaluation.
- Implement cloud‑native data pipelines connecting data lakes, warehouses, and APIs; adhere to architecture best practices, security, and compliance.
- Build and manage Docker/Kubernetes (AKS/EKS) containerized inference services.
- Create and maintain CI/CD pipelines with Azure DevOps, GitHub Actions, AWS CodePipeline, Terraform/CloudFormation.
- Monitor model performance, automate retraining, and manage model versioning.
- Ensure data privacy (HIPAA, GDPR, SOC 2) and apply responsible AI principles (fairness, transparency, explainability).
- Present technical designs, metrics, and recommendations to stakeholders.
**Required Skills**
- Python, R, TensorFlow, PyTorch, scikit‑learn, Hugging Face Transformers.
- Azure AI services (ML, Cognitive Services, Databricks, Data Lake, Synapse).
- AWS AI/ML services (SageMaker, Rekognition, Comprehend, S3, Redshift, Kinesis, CloudFormation).
- Containerization (Docker) and orchestration (Kubernetes AKS/EKS).
- CI/CD tooling (Azure DevOps, GitHub Actions, AWS CodePipeline).
- Infrastructure as Code (Terraform, CloudFormation).
- Big data frameworks (Spark, Databricks).
- Strong analytical, problem‑solving, and communication skills.
**Required Education & Certifications**
- Bachelor’s or Master’s in Computer Science, Data Science, AI/ML, or related field.
- 5+ years of AI/ML solution development and deployment experience.
- 3+ years of hands‑on experience with Azure and AWS AI/ML services.
- Optional certifications: Microsoft Certified: Azure AI Engineer Associate / Azure Solutions Architect Expert; AWS Certified Machine Learning – Specialty / AWS Solutions Architect – Professional.