AI Platform Engineer BFSI Domain

Expleo

6 - 11 years

20 - 25 Lacs

mumbai

Posted:15 hours ago| Platform:

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Skills Required

automation backend front end postgresql hipaa management middleware monitoring sql python

Work Mode

Work from Office

Job Type

Full Time

Job Description

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) 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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