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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, workplace locking, and artifacts suitable for promotion across stages.

• Apply testing & quality: pytest, unit/integration/e2e tests,

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