Job Title: Data Engineer - AI/ML Engineer

Location: Remote/Pune

Job Type: Full Time The shift timing: 2pm to 11pm

Responsibilities

- Design, build, and maintain scalable data pipelines to support analytics, ML, and operational reporting.
- Develop robust data ingestion, transformation, and integration workflows using Python, SQL, and modern data engineering frameworks.
- Build and maintain batch and streaming data pipelines leveraging technologies such as Kafka (or similar pub/sub tools).
- Work with Google Cloud Platform (GCP) services, including Cloud Storage, Dataflow, Pub/Sub, BigQuery, Cloud Spanner and Cloud Functions.
- Develop and manage data APIs and interfaces (REST and GraphQL) to enable high-performance data access across microservices.
- Implement CI/CD automation for data pipelines using GitHub Actions, Argo CD, or equivalent tools.
- Collaborate with Data Scientists and MLOps teams to integrate ML/NLP models into data pipelines and production workflows.
- Build and operationalize NLP data pipelines for structured and unstructured data sources (e.g., Rx claims, clinical documents).
- Enable continuous learning and model-retraining workflows using Vertex AI, Kubeflow, or similar GCP-native tooling.
- Implement frameworks for observability and data quality, ensuring ML predictions, confidence scores, and fallback events are logged into data lakes or monitoring systems.
- Support distributed data systems and ensure reliability, performance, and scalability of data infrastructure.

Infrastructure Engineering Responsibilities
- Design and provision cloud infrastructure using Infrastructure as Code (IaC) tools such as Terraform or Pulumi for GCP resources including GKE clusters, Cloud SQL, VPC networks, IAM, and storage.
- Deploy, configure, and manage containerized data workloads using Kubernetes (GKE) — including deployments, autoscaling (HPA/VPA), namespaces, resource quotas, and health checks.
- Architect and maintain network topology for data platform environments — VPCs, subnets, firewall rules, private service access, Cloud NAT, and VPC Service Controls.
- Implement and enforce IAM policies, service account governance, and secrets management (GCP Secret Manager or HashiCorp Vault) to ensure least-privilege access across all data services.




- Build and maintain infrastructure monitoring and alerting using Cloud Monitoring, Prometheus, Grafana, or equivalent — covering pipeline latency, throughput, error rates, and resource utilization.
- Establish and maintain CI/CD pipelines for infrastructure changes using Terraform Cloud, GitHub Actions, or Argo CD, ensuring infrastructure drift detection and rollback capability.
- Manage workplace parity (dev/staging/prod) for data platform infrastructure, including environment-specific configuration management and promotion workflows.
- Drive cloud cost governance — right-sizing compute resources, implementing committed-use discounts, setting up budget alerts, and producing cost attribution reports per workload.
- Design and implement disaster recovery, backup, and high-availability strategies for data stores, pipeline infrastructure, and ML serving endpoints.
- Collaborate with Security and Platform teams to ensure data infrastructure compliance with enterprise security policies, SOC 2, HIPAA, and CVS Health regulatory requirements.

Required Qualifications
- 5+ years of experience building data pipelines or backend data workflows using Python, Java, or similar languages.
- 2+ years of experience designing REST/GraphQL data services or integrating data APIs.
- Hands-on experience working with ML/AI model integration in production (e.g., Vertex AI Endpoints, TensorFlow Serving, ML REST APIs).
- Experience handling structured and unstructured datasets, including healthcare data (Rx claims, clinical documents, NLP text).
- Familiarity with the end-to-end ML lifecycle: data ingestion, feature engineering, training, deployment, and real-time inference.
- 2+ years of experience with cloud platforms (GCP preferred; AWS or Azure acceptable).
- 2+ years working with streaming platforms like Kafka or equivalent.
- 2+ years of experience with databases (Postgres or similar relational systems).




- 2+ years of experience with CI/CD tools (GitHub Actions, Jenkins, Argo CD, etc.).
- 2+ years of hands-on experience with Infrastructure as Code tools (Terraform preferred; Pulumi or CDK acceptable).
- 2+ years managing containerized workloads using Kubernetes (GKE, EKS, or AKS) — deploying services, configuring autoscaling, and managing resource limits.
- Solid understanding of cloud networking fundamentals: VPCs, subnets, firewall rules, private connectivity, and DNS resolution in GCP or AWS.
- Experience designing IAM roles, service accounts, and secrets management workflows to enforce least-privilege access across data services.
- Familiarity with infrastructure monitoring and alerting tools (Cloud Monitoring, Prometheus/Grafana, or equivalent).

Preferred Qualifications
- Direct, hands-on experience with Google Cloud Platform, especially BigQuery, Dataflow, GKE, Composer and Vertex AI.
- Knowledge of Kubernetes concepts and experience running data services or pipelines on GKE.
- Strong understanding of distributed systems, microservice patterns, and data-centric system design.
- Experience using Vertex AI, Kubeflow, or other ML orchestration platforms for model training and serving.
- Knowledge of GenAI pipelines, LLM prompt workflows, and agent orchestration frameworks (e.g., LangChain, transformers).
- Experience deploying Python-based ML/NLP services into microservice ecosystems using REST, gRPC, or sidecar architectures.
- Domain experience in healthcare, claim adjudication, or Rx data processing.
- Experience with Terraform modules, workspaces, and remote state backends for managing multi-environment GCP infrastructure.
- Familiarity with GCP Shared VPC, VPC Service Controls, or private Google Access configurations for secure data platform networking.
- Exposure to FinOps practices — cloud cost attribution, showback/chargeback models, and resource tagging strategies.
- Experience with GitOps workflows using Argo CD or Flux for managing infrastructure and application delivery.

Education
- Bachelor's degree in Computer Science, Data Engineering, Information Systems, or equivalent experience (High School Diploma + 4 years of relevant experience acceptable).

📌 Data Engineer - AI/ML Engineer (Pune)
🏢 IntraEdge
📍 Pune