- Company Name
- Syensqo
- Job Title
- Senior AI Engineer
- Job Description
-
**Job Title:** Senior AI Engineer
**Role Summary:**
Design, build, and productionize agentic and generative AI solutions on Azure for Finance and Corporate functions. Lead AI proof‑of‑value projects, ensure scalability, reproducibility, and compliance, and serve as a technical bridge between business stakeholders and engineering teams.
**Expectations:**
- Deliver secure, compliant AI‑enabled workflows that generate measurable business value.
- Own end‑to‑end AI product lifecycle: planning, architecture, implementation, MLOps, and monitoring.
- Provide technical leadership, set standards, and mentor junior engineers.
- Collaborate closely with Finance, IT, and Security teams to align solutions with enterprise policies and KPIs.
**Key Responsibilities:**
- Translate Finance/Corporate stakeholder requirements into AI solutions (agents, GPTs, document‑intelligence pipelines).
- Design and deploy LLM‑based agents with function calling, Azure OpenAI, and tool integrations.
- Build Retrieval‑Augmented Generation (RAG) pipelines using Azure AI Search and Document Intelligence; manage chunking, routing, safety, and evaluation.
- Orchestrate data pipelines with Azure Data Factory/Microsoft Fabric; integrate ERP/SAP and corporate APIs under identity and access controls.
- Implement Azure MLOps: CI/CD for models/prompts, versioning, telemetry, canary/rollback, cost governance.
- Ensure privacy and security by design: secret management, PII handling, audit trails, and compliance documentation.
- Define KPIs with Finance, instrument telemetry, and create dashboards to demonstrate impact.
- Deploy solutions on AKS or Azure Container Apps, meeting reliability, SLA, and policy requirements.
- Develop reusable templates, SDKs, and reference architectures; conduct demos, workshops, and create concise user guides.
**Required Skills:**
- Expert Python programming; experience with ML/LLM frameworks (PyTorch, scikit‑learn, Transformers).
- Proficiency in agent frameworks (LangChain, Semantic Kernel, LlamaIndex).
- Deep knowledge of Azure AI stack: Azure Machine Learning, Azure OpenAI/AI Foundry, Azure AI Search, Azure AI Document Intelligence, Azure Data Factory, Azure DevOps, Microsoft Fabric.
- Containerization and orchestration (Docker, AKS, Azure Container Apps).
- Data engineering fundamentals: SQL/NoSQL, ETL/ELT, API integration, secure cloud data pipelines.
- Strong understanding of MLOps best practices: CI/CD, version control, monitoring, rollback, cost management.
- Ability to assess and mitigate hallucination, bias, latency, and cost in LLM outputs.
- Excellent communication, stakeholder management, and mentorship abilities.
**Required Education & Certifications:**
- Master’s or PhD in Computer Science, Data Science, Engineering, Mathematics, or related field, or equivalent practical experience.
- Finance literacy (controlling, FP&A, accounting, procurement, treasury) is a strong plus.
- Relevant certifications (e.g., Microsoft Azure AI Engineer Associate, Azure Solutions Architect) are beneficial but not mandatory.