AI Platform Engineer – BFSI Domain

Overview

Core Technical Skills

Programming Languages

• Python (essential for ML frameworks and automation)
• Java / Scala (for data pipelines or Spark-based processing)
• Go or (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)

Responsibilities

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.

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Qualifications

Core Technical Skills

Programming Languages

• Python (essential for ML frameworks and automation)
• Java / Scala (for data pipelines or Spark-based processing)
• Go or (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)

Essential skills

Core Technical Skills

Programming Languages

• Python (essential for ML frameworks and automation)
• Java / Scala (for data pipelines or Spark-based processing)
• Go or (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)

Experience

• upto 6 years total

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