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AI Platform Engineer – BFSI Domain
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Overview:
Responsibilities:
Key Responsibilities
Qualifications:
Essential skills:
Experience:
Core Technical Skills
- Programming Languages
- Python (essential for ML frameworks and automation)
- Java / Scala (for data pipelines or Spark-based processing)
- Go or Node.js (for backend and API development)
- Data Engineering & Processing
- SQL and NoSQL databases (e.g., PostgreSQL, MongoDB)
- Data pipelines with Apache Spark, Kafka, Airflow, Beam, or Flink
- ETL/ELT development and data lake management (e.g., BigQuery, Snowflake, Databricks)
- APIs and Microservices
- REST/gRPC API design
- Containerization using Docker and orchestration via Kubernetes
AI/ML-Specific Skills
- Model Lifecycle Management
- Model training, versioning, deployment, and rollback
- MLflow, Kubeflow, Vertex AI, or SageMaker
- Frameworks and Libraries
- TensorFlow, PyTorch, Scikit-learn
- Hugging Face, LangChain (for LLM integration)
- Data Science Collaboration
- Working with Data Scientists for model reproducibility and scalability
- Feature store design and implementation (e.g., Feast, Tecton)
- AI Infrastructure
- GPU/TPU management, distributed training setups
- Optimization for inference latency and cost efficiency
- ️ MLOps / DevOps Skills
- CI/CD for ML
- Jenkins, GitHub Actions, GitLab CI, ArgoCD
- Automated testing and validation of ML pipelines
- Monitoring and Observability
- Model drift and data drift detection
- Logging, tracing, and alerting (Prometheus, Grafana, ELK stack)
- Cloud Platforms
- GCP (Vertex AI, BigQuery, Dataflow, GKE)
- AWS (SageMaker, EKS, Glue, Lambda)
- Azure (ML Studio, AKS, Data Factory)
- Security and Compliance
- IAM, RBAC, network policies, data encryption
- Compliance with data privacy (GDPR, HIPAA)
Key Responsibilities
- Platform Development and Evangelism:
- Build scalable AI platforms that are customer-facing.
- Connect Front end-Middleware and Backend
- Ensure platform scalability, reliability, and performance to meet business needs.
Machine Learning Pipeline Design:
- Design ML pipelines for experiment management, model management, feature management, and model retraining.
- Implement A/B testing of models.
- Design APIs for model inferencing at scale.
- Proven expertise with MLflow, SageMaker, Vertex AI, and Azure AI.
L
Qualifications:
Core Technical Skills
- Programming Languages
- Python (essential for ML frameworks and automation)
- Java / Scala (for data pipelines or Spark-based processing)
- Go or Node.js (for backend and API development)
- Data Engineering & Processing
- SQL and NoSQL databases (e.g., PostgreSQL, MongoDB)
- Data pipelines with Apache Spark, Kafka, Airflow, Beam, or Flink
- ETL/ELT development and data lake management (e.g., BigQuery, Snowflake, Databricks)
- APIs and Microservices
- REST/gRPC API design
- Containerization using Docker and orchestration via Kubernetes
AI/ML-Specific Skills
- Model Lifecycle Management
- Model training, versioning, deployment, and rollback
- MLflow, Kubeflow, Vertex AI, or SageMaker
- Frameworks and Libraries
- TensorFlow, PyTorch, Scikit-learn
- Hugging Face, LangChain (for LLM integration)
- Data Science Collaboration
- Working with Data Scientists for model reproducibility and scalability
- Feature store design and implementation (e.g., Feast, Tecton)
- AI Infrastructure
- GPU/TPU management, distributed training setups
- Optimization for inference latency and cost efficiency
- ️ MLOps / DevOps Skills
- CI/CD for ML
- Jenkins, GitHub Actions, GitLab CI, ArgoCD
- Automated testing and validation of ML pipelines
- Monitoring and Observability
- Model drift and data drift detection
- Logging, tracing, and alerting (Prometheus, Grafana, ELK stack)
- Cloud Platforms
- AWS (SageMaker, EKS, Glue, Lambda)
- Security and Compliance
- IAM, RBAC, network policies, data encryption
- Compliance with data privacy (GDPR, HIPAA)
Core Technical Skills
- Programming Languages
- Python (essential for ML frameworks and automation)
- Java / Scala (for data pipelines or Spark-based processing)
- Go or Node.js (for backend and API development)
- Data Engineering & Processing
- SQL and NoSQL databases (e.g., PostgreSQL, MongoDB)
- Data pipelines with Apache Spark, Kafka, Airflow, Beam, or Flink
- ETL/ELT development and data lake management (e.g., BigQuery, Snowflake, Databricks)
- APIs and Microservices
- REST/gRPC API design
- Containerization using Docker and orchestration via Kubernetes
AI/ML-Specific Skills
- Model Lifecycle Management
- Model training, versioning, deployment, and rollback
- MLflow, Kubeflow, Vertex AI, or SageMaker
- Frameworks and Libraries
- TensorFlow, PyTorch, Scikit-learn
- Hugging Face, LangChain (for LLM integration)
- Data Science Collaboration
- Working with Data Scientists for model reproducibility and scalability
- Feature store design and implementation (e.g., Feast, Tecton)
- AI Infrastructure
- GPU/TPU management, distributed training setups
- Optimization for inference latency and cost efficiency
- ️ MLOps / DevOps Skills
- CI/CD for ML
- Jenkins, GitHub Actions, GitLab CI, ArgoCD
- Automated testing and validation of ML pipelines
- Monitoring and Observability
- Model drift and data drift detection
- Logging, tracing, and alerting (Prometheus, Grafana, ELK stack)
- Cloud Platforms
- GCP (Vertex AI, BigQuery, Dataflow, GKE)
- AWS (SageMaker, EKS, Glue, Lambda)
- Azure (ML Studio, AKS, Data Factory)
- Security and Compliance
- IAM, RBAC, network policies, data encryption
- Compliance with data privacy (GDPR, HIPAA)
- upto 6 years total
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