GenAI engineer job opportunity at Accenture Plc.



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Accenture Plc GenAI engineer
Experience: 2 years
Pattern: full-time
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AI & Data

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degreeBachelor's (B.Sc.)
loacation Helsinki, Finland
loacation Helsinki....Finland

In the era of Data & AI, the possibilities to make a change are limitless. Join our diverse team of professionals to help our Nordic and global customers design, build, and scale Generative AI solutions that drive real business value. We are now looking for Generative AI Engineers with hands-on experience in creating LLM-powered applications and platforms—covering retrieval-augmented generation (RAG), prompt engineering, model evaluation, safety & governance, and LLMOps. In your role, you will build solutions together with our clients using modern tools and cloud services, integrating the latest advances in GenAI into our clients’ core business processes. Your work will span the following areas Designing, building, and operating GenAI-enabled applications (chat, summarization, agents, copilots, content generation) for enterprise use cases Implementing RAG pipelines: document ingestion, chunking, embeddings, vector stores, retrieval strategies, and answer synthesis Prompt engineering and tool/function calling; building and orchestrating multi-step agents/workflows Establishing LLMOps foundations: CI/CD for prompts and chains, offline/online evaluation, observability, telemetry, A/B testing, and continuous improvement Integrating on‑premises and cloud data sources securely with attention to PII, data residency, and GDPR Selecting and operating model endpoints (managed APIs and self/managed hosting), including performance tuning, caching, and cost optimization Building guardrails and safety layers (grounding, policy enforcement, content filters, redaction) and aligning with Responsible AI practices Collaborating with data, platform, and application teams to land GenAI in production with robust APIs, monitoring, and SLOs You preferably have experience in some of the following (and interest to grow in the others) LLM platforms & clouds: Azure OpenAI / Azure AI, AWS Bedrock, Google Vertex AI, open‑source models (Llama, Mistral, etc.) Orchestration & agents: Semantic Kernel, LangChain, LlamaIndex, function/tool calling, planner/agent patterns RAG components: vector databases (Azure AI Search, FAISS, Milvus, Pinecone), embedding models, hybrid search, re-ranking Evaluation & quality: RAGAS/DeepEval-like frameworks, golden sets, hallucination & toxicity checks, guardrails Application dev: Python (FastAPI), TypeScript/Node.js; API design, streaming, and event-driven patterns MLOps/LLMOps: MLflow/W&B, feature & model registries, prompt/version management, experiment tracking, telemetry Data & integration: document processing (OCR, parsing), knowledge graph/metadata enrichment, connectors to enterprise systems Batch/stream processing (nice to have): Spark/Databricks, Airflow, Kafka/Flink for data prep supporting GenAI workloads Performance & infra (nice to have): containerization (Docker/Kubernetes), GPU acceleration, quantization/optimization (ONNX, vLLM, TensorRT), caching/indexing strategies Security & compliance: Responsible AI controls, GDPR & PII handling

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