EY is seeking an accomplished AI Integration Engineer with a focus on DevSecOps to join our dynamic team. This role offers an exceptional opportunity to build a career within a global organization, leveraging cutting-edge technology and a supportive, inclusive culture. We are committed to fostering a unique professional journey and value the diverse perspectives that contribute to our continuous improvement and the creation of a better working world.

As an AI Integration Engineer, you will be instrumental in designing and implementing end-to-end AI/ML solutions. This includes developing robust pipelines from training and evaluation to deployment, ensuring seamless integration and operational efficiency. Your expertise will drive innovation and enhance our capabilities in delivering advanced AI services.

Join us to build an exceptional experience for yourself and contribute to a more innovative and responsible technological future.

Design and build comprehensive AI/ML pipelines, encompassing training, evaluation, and deployment using tools like MLflow, Kubeflow, and Databricks. Develop and package Python models with PyTorch, TensorFlow, and scikit-learn into reproducible services. Implement advanced LLM/RAG systems utilizing LangChain, LlamaIndex, and vector databases for sophisticated semantic retrieval and grounding.

Optimize models through fine-tuning techniques such as PEFT/LoRA/QLoRA and quantization, exporting them efficiently via ONNX Runtime or TorchScript. Engineer scalable model serving solutions with KServe, Seldon Core, or BentoML, supporting diverse deployment strategies like A/B and canary releases. Construct evaluation harnesses, integrating them into CI/CD pipelines for continuous quality assurance.

Orchestrate event-driven data pipelines with Airflow or Prefect, and manage streaming data via Kafka or RabbitMQ. Develop Python microservices using FastAPI and gRPC, integrating them with downstream systems through REST or GraphQL. Implement robust automation using Python, Bash, and SQL for data operations. Apply rigorous testing methodologies, including unit, integration, and e2e tests, alongside linters and type checks.

Deliver Infrastructure as Code (IaC) with Terraform or Pulumi, manage configurations using Helm or Kustomize, and implement GitOps principles with Argo CD or Flux on Kubernetes. Build secure CI/CD pipelines for AI/ML artifacts, incorporating DevSecOps practices such as SAST/DAST scanning, dependency management, and policy enforcement. Manage secrets effectively with Vault and enforce AI safety and governance measures, including prompt-injection defenses and output filtering.

Monitor model and data drift, bias, and performance using tools like Evidently or Arize, unifying telemetry through OpenTelemetry and Prometheus. Optimize compute and GPU usage, track cost and latency SLOs, and implement progressive delivery strategies for services and models. Operate API gateways and service meshes, ensuring adherence to privacy and compliance standards like GDPR and ISO 27001. Collaborate with cross-functional teams to publish best practices and contribute to SRE initiatives.

We are seeking a DevSecOps & AI Engineer with 4 to 7 years of extensive experience in cloud platforms, automation, and AI/ML engineering. A strong command of Terraform, Kubernetes, Docker, and modern CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, Azure DevOps) is essential.

Proficiency in Python, particularly with FastAPI and ML libraries like PyTorch or TensorFlow, is required. Experience in scripting with Bash or PowerShell for automation is also necessary. Solid understanding and hands-on application of DevSecOps practices, including SAST/DAST, container/IaC scanning, and policy-as-code frameworks, are crucial.

Demonstrated experience in MLOps and AI integration using platforms such as MLflow, Kubeflow, Weights & Biases, KServe, or Seldon Core is expected. Familiarity with building or integrating RAG/LLM pipelines with LangChain, LlamaIndex, or vector databases is highly valued. Strong cloud fundamentals across AWS, Azure, or GCP, with the ability to architect secure, automated infrastructure via IaC and GitOps (Argo CD/Flux), are essential.

Familiarity with monitoring and observability stacks like Prometheus, Grafana, OpenTelemetry, and ELK/Loki is beneficial. Excellent troubleshooting and problem-solving skills, coupled with a collaborative, engineering-first mindset, are key. Strong communication skills to effectively work cross-functionally with Data, AI/ML, DevOps, Security, and Platform Engineering teams are required. A B.Tech. or BS in Computer Science is the educational background we are looking for.