Generative AI Engineer at Accenture

Helsinki, Uusimaa, Finland

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Not SpecifiedCompensation

Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, Data & AIIndustries

Requirements

Degree in Computer Science, Engineering, or related technical field
Preferably at least one cloud certificate (Azure, AWS, or GCP) and/or relevant AI/GenAI certifications
Understanding and interest in end-to-end GenAI solution development—from solution design and data grounding to quality assurance, safety, and production operations
Skills in modern software engineering: version control (Git), documentation, automated testing, CI/CD, and agile teamwork
Strong willingness and enthusiasm to learn and continuously develop skills in a rapidly evolving GenAI landscape
Ability to be an innovative, resourceful, and collaborative problem solver with a product mindset
Preferably experience in LLM platforms & clouds: Azure OpenAI / Azure AI, AWS Bedrock, Google Vertex AI, open-source models (Llama, Mistral, etc.)
Preferably experience in orchestration & agents: Semantic Kernel, LangChain, LlamaIndex, function/tool calling, planner/agent patterns
Preferably experience in RAG components: vector databases (Azure AI Search, FAISS, Milvus, Pinecone), embedding models, hybrid search, re-ranking
Preferably experience in evaluation & quality: RAGAS/DeepEval-like frameworks, golden sets, hallucination & toxicity checks, guardrails
Preferably experience in application dev: Python (FastAPI), TypeScript/Node.js; API design, streaming, and event-driven patterns
Preferably experience in MLOps/LLMOps: MLflow/W&B, feature & model registries, prompt/version management, experiment tracking, telemetry
Preferably experience in data & integration: document processing (OCR, parsing), knowledge graph/metadata enrichment, connectors to enterprise systems
Preferably experience in batch/stream processing: Spark/Databricks, Airflow, Kafka/Flink for data prep supporting GenAI workloads
Preferably experience in performance & infra: containerization (Docker/Kubernetes), GPU acceleration, quantization/optimization (ONNX, vLLM, TensorRT), caching/indexing strategies
Preferably experience in security & compliance: Responsible AI controls, GDPR & PII handling
Hands-on experience in creating LLM-powered applications and platforms—covering retrieval-augmented generation (RAG), prompt engineering, model evaluation, safety & governance, and LLMOps

Responsibilities

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
Building solutions together with clients using modern tools and cloud services, integrating the latest advances in GenAI into clients’ core business processes

Skills

Generative AI

LLM

RAG

Prompt Engineering

Model Evaluation

LLMOps

Azure OpenAI

AWS Bedrock

Google Vertex AI

Llama

Mistral

Vector Stores

Embeddings

CI/CD

A/B Testing

Responsible AI

Agents

Copilots

Accenture

Global professional services for digital transformation

About Accenture

Accenture provides a wide range of professional services, including strategy and consulting, technology, and operations, to help organizations improve their performance. Their services assist clients in navigating digital transformation, enhancing operational efficiency, and achieving sustainable growth. Accenture's offerings include cloud migration, cybersecurity, artificial intelligence, and data analytics, which are tailored to meet the needs of various industries such as financial services, healthcare, and retail. What sets Accenture apart from its competitors is its extensive industry knowledge and ability to deliver comprehensive solutions that address both immediate challenges and long-term goals. The company's aim is to support clients in reducing their environmental impact while driving innovation and growth.

Dublin, IrelandHeadquarters

1989Year Founded

$8.5MTotal Funding

IPOCompany Stage

Consulting, Enterprise Software, CybersecurityIndustries

10,001+Employees

Risks

Rapid AI advancements may outpace Accenture's current capabilities, risking competitive disadvantages.

Integration challenges from multiple acquisitions could affect Accenture's operational efficiency.

The rise of AI-driven startups may disrupt Accenture's market share in customer service solutions.

Differentiation

Accenture's acquisitions enhance its capabilities in digital twin technology for financial services.

The company is expanding its expertise in net-zero infrastructure through strategic acquisitions.

Accenture's focus on software-defined vehicles positions it as a leader in automotive innovation.

Upsides

Accenture's investment in EMTECH supports central bank modernization amid digital currency evolution.

The acquisition of Award Solutions boosts Accenture's presence in the growing 5G and IoT markets.

Accenture's strategic acquisitions align with high-growth markets like digital twins and net-zero projects.

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