Design and build an agentic AI platform to manage renewable energy certificates and automate related workflows. Collaborate across teams to integrate the platform, optimize user onboarding, and iterate based on feedback and metrics.
About the role
- AI Engineer working with Generative AI and Large Language Models at a global energy and petrochemical company. Responsible for designing, deploying, and integrating AI solutions with measurable impact.
Responsibilities
- Design, build, and deploy LLM-powered applications leveraging frameworks such as LangChain and LlamaIndex.
- Integrate Generative AI APIs (OpenAI, Anthropic, Cohere, Mistral) into enterprise systems, ensuring scalability and reliability.
- Collaborate with data scientists, DevOps, and product teams to deliver production-ready GenAI solutions.
- Develop retrieval-augmented generation (RAG) systems combining LLMs with enterprise data using vector databases (FAISS, Weaviate, Pinecone, Qdrant).
- Lead model deployment using tools like Flask, FastAPI, MLflow, and Triton Inference Server.
- Ensure responsible AI practices through bias detection, content filtering, explainability, and hallucination mitigation.
- Build and automate evaluation pipelines to track key metrics such as relevance, response quality, and drift detection in production.
- Engage in rapid prototyping to test and deliver AI-driven MVPs supporting digital transformation initiatives.
- Collaborate on AI system architecture design, focusing on performance, robustness, and maintainability.
- Drive continuous improvement in GenAI safety layers, guardrails, and prompt optimization.
Requirements
- Strong programming proficiency in Python.
- Hands-on experience with data processing tools such as NumPy, Pandas, and SQL.
- Proven experience with model deployment and serving frameworks (Flask, FastAPI, MLflow, Triton).
- Proficiency with LLM frameworks (LangChain, LlamaIndex) and vector databases (FAISS, Weaviate, Pinecone, or Qdrant).
- Experience integrating APIs from OpenAI, Anthropic, Cohere, or Mistral.
- Good understanding of MLOps principles: versioning, monitoring, and logging.
- Familiarity with LLM safety practices, including hallucination mitigation and prompt validation.
- Strong grasp of evaluation and monitoring techniques for GenAI models (e.g., A/B testing, MLflow tracking).
- Excellent collaboration and communication skills; able to work cross-functionally.
- Experience with Azure Cloud is a plus.
- Knowledge of Oil & Gas, Shipping & Trading, or ETRM business domains is a strong advantage.
Benefits
- Competitive salary based on experience
- Training and learning opportunities
- Equipment budget
- Private healthcare
- Remote work options
- Birthday gifts
- MultiSport card
- Wellbee mental health package
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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