AI Integration Engineer-Senior

Location: Bengaluru

Other locations: Primary Location Only

Salary: Competitive

Date: Jun 2, 2026

• *Job description**
• ------------------

Requisition ID: 1695731

At EY, we’re all in to shape your future with confidence.

We’ll 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.

• *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.

• *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.

• *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.