Generative AI / Agentic AI Senior Engineer

What you need to succeed:

Core Engineering Fundamentals (Must-Have)

• Proficiency in Python as the primary language for AI development.

• Solid understanding of APIs, REST/GraphQL, and integration patterns.

• Experience working with databases — both SQL (e.g., PostgreSQL, MySQL) and NoSQL (e.g., MongoDB, DynamoDB, CosmosDB).

• Familiarity with version control (Git), testing, and CI/CD pipelines for automated deployments.

Generative AI Foundations

• Hands-on experience using Large Language Models (LLMs) such as GPT, Claude, Gemini, or Llama.

• Practical skill in prompt engineering (zero-shot, few-shot, chain-of-thought, and dynamic prompting).

• Understanding of tokenization, context windows, and model inference constraints — i.e., how model size, context limits, latency, and cost impact runtime behavior and system design.

• Ability to design and implement retrieval-augmented generation (RAG) pipelines for enterprise or domain-specific contexts.

Data & Knowledge Systems

• Familiarity with embedding models and vectorization techniques for semantic search, context retrieval, and personalization.

• Experience using vector databases such as FAISS, Pinecone, Weaviate, Chroma or similar

• Competence in data preprocessing, indexing, and pipeline optimization for large-scale text or multi-modal data.

Cloud Infrastructure & MLOps

• Experience deploying AI workloads on cloud platforms (e.g., Azure, GCP, AWS, or private cloud).

• Familiarity with containerization (Docker) and orchestration (Kubernetes) for scalable AI services.

• Understanding of infrastructure-as-code tools (e.g Terraform, Pulumi or CloudFormation) and monitoring/observability frameworks (Prometheus, Grafana, OpenTelemetry).

• Exposure to CI/CD, model deployment pipelines, and cloud-based inference endpoints.

Agentic AI & Orchestration

• Knowledge of agent frameworks (e.g., LangChain, AutoGen, CrewAI, LlamaIndex) and agent-based reasoning architectures — systems where LLMs can plan, act, and collaborate through structured reasoning loops (e.g., ReAct, planner–executor, or multi-agent designs).

• Experience integrating LLMs with tools, APIs, and knowledge bases to enable autonomous or semi-autonomous workflows.

• Ability to design multi-agent systems that coordinate task planning, execution, and evaluation.

Advanced nice to have skills

• Familiarity with fine-tuning, LoRA, or adapter-based customization for domain-specific model behavior.

• Understanding of embedding optimization, context compression, and persistent memory architectures for long-running agents.

• Experience with distributed inference, GPU optimization, or scalable API serving for LLM-backed systems.

• Awareness of evaluation metrics for LLM performance, hallucination control, and reasoning consistency.

• Experience designing multi-modal systems combining text, image, or structured data reasoning.

Your life at Accenture

At Accenture, we prioritize your health and well-being through comprehensive health insurance coverage and inclusive work arrangements. We value your growth and offer diverse learning and development paths, along with performance-based rewards. You'll also have access to resources for mental health and physical wellness, ensuring inclusivity and support. We provide additional financial support, flexible benefits, and care for your loved ones, respecting diverse circumstances.

Join us for a fulfilling career and a balanced lifestyle that celebrates diversity and promotes inclusivity.

The position is available exclusively for collaboration under an individual working contract (CIM).

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Egalité des chances face à l'emploi

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