Tech S And T - AI Integration Engineer Senior - GDSN02

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. 

Designation    AI Integration Engineer

Job Description

• Build end‑to‑end AI/ML pipelines (training → evaluation → deployment) using MLflow/Kubeflow/Databricks/Weights & Biases with experiment tracking and model registries.
• Develop models with Python using PyTorch, TensorFlow, JAX, scikit‑learn, and Hugging Face Transformers, package as reproducible services.
• Implement LLM/RAG systems with LangChain, LlamaIndex, Semantic Kernel and vector DBs (Pinecone, Weaviate, Milvus, FAISS, Chroma) for semantic retrieval and grounding.
• Fine‑tune and optimize models using PEFT/LoRA/QLoRA, DeepSpeed/Accelerate, distillation, and quantization; export/optimize via ONNX Runtime/TorchScript/TensorRT.
• Engineer scalable model serving with KServe, Seldon Core, BentoML, Ray Serve, NVIDIA Triton, supporting A/B, canary, shadow deployments.
• Build evaluation harnesses (offline/online) with Ragas, TruLens, Promptfoo, golden datasets, and regression gates integrated into CI/CD.
• Construct feature stores (e.g., Feast) and data contracts (Protobuf/Avro/Pydantic); enforce data quality with Great Expectations/Deequ.
• Orchestrate event‑driven pipelines with Airflow/Prefect/Dagster; streaming/messaging via Kafka/RabbitMQ/NATS and schema registries.
• Design Python microservices using FastAPI/gRPC; integrate with downstream systems via REST/GraphQL; write robust automation in Python/Bash/PowerShell and SQL for data ops.
• Use notebooks (Jupyter) and packaging (Poetry/pip/conda) with virtualenvs, environment locking, and artifacts suitable for promotion across stages.
• Apply testing & quality: pytest, unit/integration/e2e tests, property‑based (hypothesis), linters/formatters (ruff/flake8, black), type checks (mypy/pyright), pre‑commit.
• Deliver IaC with Terraform/Pulumi; manage config via Helm/Kustomize; implement GitOps with Argo CD/Flux on managed/self‑hosted Kubernetes.
• Build secure CI/CD (GitHub Actions/GitLab CI/Jenkins/Azure DevOps) for app/data/ML artifacts, artifact promotion, provenance, and automated rollbacks.
• Embed DevSecOps: SAST/DAST/IAST (Snyk/Checkmarx/SonarQube), container & IaC scanning (Trivy), dependency hygiene (Dependabot/Renovate), SBOM (Syft/CycloneDX).
• Enforce policy‑as‑code (OPA/Gatekeeper, Kyverno), image signing/verification (Sigstore/cosign), supply‑chain standards (SLSA, in‑toto).
• Manage secrets/KMS with Vault and native managers; adopt short‑lived workload identities, mTLS, and least‑privilege RBAC/ABAC in clusters and pipelines.
• Implement AI safety & governance: prompt‑injection defenses, output filtering, PII redaction, guardrails (Guardrails.ai/NeMo Guardrails/Presidio), policy checks.
• Monitor model/data drift, bias, and performance with Evidently/WhyLabs/Arize/Fiddler; unify telemetry via OpenTelemetry, Prometheus, Grafana, ELK/Loki, Jaeger.
• Optimize compute/GPU: CUDA/cuDNN/NCCL, HPA/VPA/KEDA, efficient batching, caching, concurrency control; track cost and latency SLOs.
• Implement progressive delivery for services/models (blue/green, canary, shadow) using Argo Rollouts/Flagger with instant rollback and health checks.
• Operate API gateways and service mesh (Kong/NGINX/Envoy, Istio/Linkerd) for rate limiting, mTLS, authN/Z, and zero‑trust patterns.
• Ensure privacy/compliance (GDPR/CCPA/DPDP/ISO 27001): data minimization, masking/tokenization, DLP, lineage (OpenLineage/Marquez), model cards/data sheets.
• Collaborate with security, data, and platform teams to publish golden paths, templates, and reference implementations for repeatable AI delivery.
• Contribute to code/design reviews and SRE practices (SLIs/SLOs/error budgets), on‑call readiness, incident response, and blameless post‑mortems.

Desired Profile 

• Looking for a DevSecOps & AI Engineer with 4–7 years of hands‑on experience in cloud platforms, automation, and AI/ML engineering workflows.
• Strong expertise in Terraform, Kubernetes, Helm, Docker, and modern CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, or Azure DevOps.
• Proficient in Python with experience in FastAPI, ML libraries (PyTorch/TensorFlow), and scripting using Bash or PowerShell for automation.
• Solid experience in DevSecOps practices including SAST/DAST, container/IaC scanning, secrets scanning, SBOM, and policy-as-code frameworks.
• Hands‑on exposure to MLOps and AI integration using tools like MLflow, Kubeflow, Weights & Biases, KServe, Seldon Core, or BentoML.
• Experience building or integrating RAG/LLM pipelines using LangChain, LlamaIndex, or vector databases (Pinecone/FAISS/Weaviate).
• Strong cloud fundamentals across AWS/Azure/GCP with ability to architect secure, automated infrastructure via IaC and GitOps (Argo CD/Flux).
• Familiarity with monitoring and observability stacks (Prometheus, Grafana, OpenTelemetry, ELK/Loki) for application and model performance.
• Strong troubleshooting, problem‑solving, and system debugging skills with a collaborative, engineering‑first mindset.
• Excellent communication skills with ability to work cross‑functionally with Data, AI/ML, DevOps, Security, and Platform Engineering teams.

Experience      4 to 7 years

Education        B.Tech. / BS in Computer Science

Technical Skills & Certifications

• Terraform, Pulumi, and IaC for automated cloud and platform provisioning.
• Kubernetes, Docker/Podman, Helm, and Kustomize for container orchestration and packaging.
• CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, and Azure DevOps.
• Proficient in Python (FastAPI, ML/LLM libraries) and scripting with Bash/PowerShell.
• DevSecOps tooling: Snyk, SonarQube, Trivy, Checkmarx, GitLeaks, and secret scanning.
• MLOps platforms: MLflow, Kubeflow, W&B, Azure ML, Vertex AI for model lifecycle management.
• Model serving frameworks: KServe, Seldon Core, BentoML, Ray Serve for scalable inference.
• RAG/LLM integration: LangChain, LlamaIndex, vector DBs (Pinecone, Weaviate, FAISS, Chroma).
• Monitoring & observability: Prometheus, Grafana, ELK/Loki, OpenTelemetry, Jaeger.
• GitOps tools (Argo CD, Flux), configuration management (Ansible/Puppet), and serverless functions.

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