Senior Software Engineer – ML Model Compliance

2 days ago

Jaipur, Rajasthan, India InfoObjects Full time ₹ 8,00,000 - ₹ 12,00,000 per year
Senior Software Engineer – ML Model Compliance & Automation

Role Category: Programming & Design

Role: Full Stack Java Developer

Job Location: Jaipur or Remote

Experience: Experience Required: 3 - 7 yrs

Employment Type: Full-time

Immediate joiner

About the Role

We are seeking a highly skilled and motivated Senior Software Engineer to lead the development and automation of ML model compliance validation workflows. This role focuses on packaging, profiling, optimization, and deployment of ML models across cloud-native environments. You will design and build tooling, pipelines, and automation frameworks to ensure models are production-ready, compliant, secure, and seamlessly integrated into CI/CD workflows.

This is a hands-on role for an engineer passionate about bridging machine learning, DevOps, and security compliance.

Key Responsibilities

Model Packaging & Artifact Management

  • Design and implement workflows for packaging ML models using KitOps, ONNX, MLflow, or TensorFlow SavedModel.
  • Manage model artifact versioning, registries, and reproducibility.
  • Ensure artifact integrity, consistency, and traceability across CI/CD pipelines.

Model Profiling & Optimization

  • Automate model profiling (latency, size, ops) using MLModelCI, TorchServe, or ONNX Runtime.
    Apply quantization, pruning, and format conversions (e.g., FP32- INT8) for optimization.
  • Embed profiling and optimization checks into CI/CD pipelines to assess deployment readiness.

Compliance & SBOM Generation

  • Develop pipelines to generate and validate SBOMs for ML models.
  • Implement compliance checks for licensing, vulnerabilities, and security using CycloneDX, SPDX, Syft, or Trivy.
  • Validate schema, dependencies, and runtime environments for production readiness.

Cloud Integration & Deployment

  • Automate model registration, endpoint creation, and monitoring setup in AWS/GCP/Azure.
  • Build cloud-native workflows using GitOps, ArgoCD, or KubeFlow for deployment and lifecycle management.
  • Collaborate with ML engineers and DevOps teams to streamline secure model delivery.

Tech Stack & Tools

  • Languages: GoLang (preferred), Python (preferred), Bash
  • MLOps Tools: KitOps, MLModelCI, MLflow, ONNX, TensorFlow, PyTorch, Docker
  • SBOM & Security: Syft, Grype, Trivy, CycloneDX, SPDX
  • CI/CD: GitHub Actions, GitLab CI, Jenkins, ArgoCD
  • Infra: Kubernetes, Docker, Helm, Terraform
  • Cloud: AWS, GCP, Azure (EKS/GKE/ECS preferred)
  • Version Control: Git, GitOps

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