Senior AI Integration Engineer

Role Overview

• 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

Requirements

• 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

Tech Stack

• Airflow
• AWS
• Azure
• Cloud
• Docker
• Flux
• Google Cloud Platform
• Grafana
• GraphQL
• GRPC
• Jenkins
• Kafka
• Kubernetes
• Microservices
• NGINX
• Prometheus
• Python
• PyTorch
• RabbitMQ
• Ray
• SQL
• Tensorflow
• Terraform
• Vault

Benefits

• Competitive salary
• Flexible working hours
• Professional development opportunities