AI Platform Engineer – BFSI Domain

4 days ago

Mumbai, Maharashtra, India Expleo Group Full time ₹ 8,00,000 - ₹ 24,00,000 per year

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.

L
Qualifications
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
Experience

  • upto 6 years total

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