Principal DevSecOps Engineer - AI

Finastra; Pune District; On-site; INR 3,500,000 - 6,200,000; Full time

Job summary

Finastra is seeking a Principal DevSecOps Engineer to secure AI workloads across CI/CD, model registries, and vector databases. You will design secure pipelines, enforce policy-as-code, and implement data-poisoning safeguards and prompt-injection mitigation.

You will harden AI serving infrastructure with IaC templates, GitOps, and strict identity patterns, while ensuring multi‑region, fault‑tolerant deployments and end‑to‑end supply chain traceability for datasets and models.

Qualifications

• 8–10 years of DevSecOps experience focusing on ML/AI security and supply chain security.
• Cloud security expertise across Azure, AWS or GCP with Kubernetes/service mesh.
• Proficiency in Python or Go and strong IaC (Terraform) and GitOps practices.

Responsibilities

• Architect secure AI platforms and scalable CI/CD pipelines for AI workloads.
• Embed automated security controls (SAST, SCA, image scanning) in pipelines.

Skills

DevSecOps

ML/AI SecOps

Cloud Security

Python

Go

Terraform

Kubernetes

Service Mesh

Vault

IaC

Tools

Terraform

Kubernetes

Service Mesh

Vault

SLSA/Sigstore

Job description

## Principal DevSecOps Engineer - AIApply: Bengaluru: Pune: Full time: Posted Today: End Date: October 2, 2026 (9 days left to apply): REQ0326\\_0036837# **Who are we?**At Finastra, we’re a global leader in financial services software, dedicated to expanding access to financial services and shaping what’s next for the industry. Our technology powers mission‐critical solutions across Lending, Payments and Universal Banking, supporting over 7,000 customers, including 80% of the world’s top 50 banks, in more than 110 countries.A senior technical SME responsible for enabling secure, automated delivery of machine learning and AI workloads. This role operates at the intersection of AI/ML engineering, product development, platform infrastructure, and cybersecurity—embedding security controls across the entire AI lifecycle, including CI/CD pipelines, model registries, vector databases, MCP services, and agent‐based AI orchestration. **Key Responsibilities:****Secure AI Platform Engineering*** Architect and implement secure, scalable, and resilient CI/CD pipeline’s purpose‐built for AI workloads, including LLM‐based and agentic AI systems.* Automate secure build, deployment, and promotion workflows for AI workloads.**ML/AI SecOps Integration*** Shift security *left* by embedding automated controls such as SAST, SCA, container/image scanning, and policy‐as‐code into pipelines.* Implement AI‐specific security measures, including data‐poisoning safeguards, model integrity validation, and prompt‐injection mitigation.**Infrastructure & Platform Security*** Work with platform engineering teams to harden vector databases, retrieval APIs, and other AI-serving infrastructure.* Establish secure Infrastructure‐as‐Code (IaC) templates, GitOps workflows, and micro‐segmented identity patterns using secrets‐management tools such as Vault.* Ensure multi‐region, fault‐tolerant, and security‐hardened deployment architectures for AI platforms.**Model Supply Chain Security*** Implement provenance, attestation, and traceability for datasets, training pipelines, and model artifacts.* Protect the end‐to‐end AI supply chain—from data ingestion through model deployment—to prevent tampering, data integrity issues, or malicious model substitution.**AI Observability & Incident Response*** Collaborate with AI Platform and Security teams to develop monitoring and detection capabilities for AI‐specific threats, including adversarial examples, model extraction attempts, drift anomalies, and suspicious agent behaviors.* Contribute to AI‐aware incident response procedures and threat‐hunting playbooks.**Governance & Compliance*** Champion the adoption of secure AI frameworks (e.g., NIST AI RMF) and define guardrails for responsible AI operations.* Establish and maintain an AI Bill of Materials (AI‐BOM) to manage risk associated with third‐party models, components, and datasets. **Leverage AI-driven insights and trusted data to enhance productivity, optimise workflows, and enable faster, higher-quality business decisions while applying sound human judgement and adapting to evolving capabilities.** **Required Qualifications:**· **8–10 yrs DevSecOps experience, ML/AI SecOps, supply chain security.****· Cloud Security expertise in securing Azure, AWS or GCP environment, particularly Kubernetes/Service Mesh, and their AI services**· **Mandatory Experience Python/Go**, or similar programming language, along with advanced knowledge of IaC (Terraform) and GitOps patterns**Preferred Skills:*** Experience with provenance/attestation frameworks (e.g., SLSA, Sigstore).* **Cloud security certifications (Azure, AWS, GCP).*** **Hands‐on experience with KMS, secrets‐management platforms, and hardware‐rooted trust technologies.**