Posted 6 days ago
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**AI Integration Engineer-Senior** at EY builds and optimizes AI/ML pipelines, from training to deployment. Key responsibilities include: - Designing, building, and optimizing end-to-end ML pipelines using tools like MLflow, Kubeflow, Databricks, and Weights & Biases. - Developing models using Python, PyTorch, TensorFlow, JAX, scikit-learn, and Hugging Face Transformers, and packaging them as services. - Implementing LLM/RAG systems with LangChain, LlamaIndex, and vector databases for semantic retrieval and grounding. - Fine-tuning and optimizing models with PEFT, LoRA, QLoRA, DeepSpeed, Accelerate, distillation, and quantization techniques. - Engineering scalable model serving with KServe, Seldon Core, BentoML, Ray Serve, and NVIDIA Triton for A/B, canary, and shadow deployments. - Building evaluation harnesses with Ragas, TruLens, Promptfoo, golden datasets, and regression gates integrated into CI/CD pipelines. - Managing feature stores (e.g., Feast) and data contracts (Protobuf/Avro/Pydantic), and ensuring data quality with Great Expectations/Deequ. - Creating automated event-driven pipelines with Airflow, Prefect, Dagster, Kafka, RabbitMQ, and NATS, and managing schema registries. - Designing Python microservices with FastAPI/gRPC, integrating with downstream systems via REST/GraphQL and writing automation scripts in Python/Bash/PowerShell and SQL. - Applying efficient MLOps techniques: packaging with Poetry/pip/conda, environment locking, artifact promotion, GitOps,
- Compensation
- Not specified
- City
- Chennai
- Country
- India
Currency: Not specified
Full Job Description
At EY, were all in to shape your future with confidence.
Well help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go.
Join EY and help to build a better working world.
Designation: AI Integration Engineer
Job Description:
- Build endtoend 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, scikitlearn, 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.
- Finetune 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 eventdriven 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, propertybased (hypothesis), linters/formatters (ruff/flake8, black), type checks (mypy/pyright), precommit.
- Deliver IaC with Terraform/Pulumi; manage config via Helm/Kustomize; implement GitOps with Argo CD/Flux on managed/selfhosted 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 policyascode (OPA/Gatekeeper, Kyverno), image signing/verification (Sigstore/cosign), supplychain standards (SLSA, intoto).
- Manage secrets/KMS with Vault and native managers; adopt shortlived workload identities, mTLS, and leastprivilege RBAC/ABAC in clusters and pipelines.
- Implement AI safety & governance: promptinjection 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 zerotrust 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), oncall readiness, incident response, and blameless postmortems.
Desired Profile
- Looking for a DevSecOps & AI Engineer with 47 years of handson 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.
- Handson 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, problemsolving, and system debugging skills with a collaborative, engineeringfirst mindset.
- Excellent communication skills with ability to work crossfunctionally 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.
EY | Building a better working world
EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets.
Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow.
EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.
