Data Engineering and Integration: Build the data and knowledge pipelines required for AI use cases, with a focus on reliable ingestion, preparation, retrieval, and access-control-aware use of information.
Technical Skills• Snowflake platform development and strong SQL - Mandatory• Python and Snow park development - Mandatory• Data engineering, data modelling, data quality and reconciliation - Mandatory• Apache Airflow or equivalent workflow orchestration - Mandatory• Snowflake Cortex AI, Cortex Analyst, Cortex Search, Cortex Agents and semantic views - Mandatory• Retrieval-augmented generation, embeddings, prompt design and agent workflows- Mandatory• Stream lit, REST APIs, Git and CI/CD practices - Good to have• Qlik dashboard development and production support - Good to haveDomain and Delivery Skills• Investment management, sustainability or financial-services data - Good to have• Enterprise data and application support operations - Mandatory• Testing, release, incident, problem and change management - Mandatory• Security, access control, auditability and responsible AI practices -Mandatory• Strong stakeholder communication and facilitation – Mandatory
AI Engineering, Governance & Scalability: Establish enterprise-grade standards for LLM observability, model evaluation (AUROC, SHAP interpretability), automated data lineage, and ethical AI governance to guarantee single-source-of-truth reliability across all distributed decision engines.
Sales & RFQ Support: Partner with regional business development teams on complex tenders, market rate benchmarking, spot pricing, and new project implementations.
Be accountable for the effectiveness and the safety of processes associated with this specific positions duty & responsibility as identified in the IMS
To review + ensure alignment of shipping instructions including clarifying any deviations or conflicts while taking country regulations, export/import compliance, rate + routing information into consideration.
Job SummaryWe are looking for a hands-on AI Engineer to design, develop, integrate, and deploy Generative AI, AI chatbot, and Agentic AI solutions. The role will focus on building practical AI applications and integrating LLM capabilities with existing applications, APIs, databases, and enterprise systems.Key ResponsibilitiesDesign and develop AI chatbots, copilots, and LLM-powered applications.Build Agentic AI solutions with reasoning, tool/function calling, multi-step task execution, and workflow automation.Implement RAG, embeddings, vector search, prompt engineering, and conversational memory.Work with commercial and open-source LLMs and select appropriate models based on business and technical requirements.Integrate AI solutions with REST APIs, databases, CRM/ERP systems, and enterprise applications.Develop AI integration services and APIs for existing systems and business workflows.Evaluate and optimize AI solutions for accuracy, reliability, latency, scalability, security, and cost.Collaborate with software engineers, architects, product teams, and business stakeholders.Keep up to date with developments in Generative AI, LLMs, Agentic AI, and AI automation.RequirementsBachelor's degree in Computer Science, AI/ML, Software Engineering, or a related field.3+ years of experience in AI/ML engineering, software engineering, or a related role.Hands-on experience developing LLM applications, AI chatbots, or Generative AI solutions.Practical experience with AI Agents, Agentic AI, tool/function calling, or AI workflow automation.Experience with open-source LLMs such as Llama, Qwen, Mistral, Gemma, or equivalent.Strong Python programming and software engineering skills.Experience with REST APIs, system integration, databases, and cloud/on-premise environments.Hands-on experience with RAG, vector databases, embeddings, and prompt engineering.Experience with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or equivalent.Experience with LLM platforms such as OpenAI, Azure OpenAI, Anthropic, Gemini, and/or open-source models.Familiarity with Docker, Git, CI/CD, and production deployment.Preferred SkillsExperience serving open-source LLMs using vLLM, Hugging Face, Ollama, or equivalent.Experience with MCP, multi-agent systems, LLM evaluation/observability, fine-tuning, LoRA/QLoRA, or model optimization.Experience with AWS, Azure, or Google Cloud.Knowledge of AI security, data privacy, access control, and responsible AI.