- Company Name
- Lazard
- Job Title
- AI Engineer
- Job Description
-
**Job Title**
AI Engineer
**Role Summary**
Design, develop, and deploy large‑scale AI and GenAI applications that generate actionable insights for financial advisory and asset‑management functions. Collaborate with investment bankers, asset managers, data scientists, and software engineers to translate complex technical models into business strategies.
**Expactations**
* Deliver high‑quality, production‑ready AI solutions that drive measurable business impact.
* Communicate model results and strategic recommendations clearly to non‑technical stakeholders.
* Work independently and as part of cross‑functional teams.
* Maintain up‑to‑date knowledge of GenAI, LLMs, and MLOps tools.
**Key Responsibilities**
1. Build and productionize GenAI applications using Python, TensorFlow, PyTorch, and scikit‑learn.
2. Develop and deploy LLM‑based solutions with LangChain, LlamaIndex, VectorDBs, and retrieval‑augmented generation (RAG) techniques.
3. Design, expose, and consume AI services via FastAPI or Flask microservices.
4. Manage data ingestion, feature engineering, and model training pipelines on Azure, AWS, or GCP.
5. Implement MLOps workflows using MLflow, Kubeflow, or Vertex AI; automate CI/CD for AI models.
6. Perform NLP tasks (sentiment analysis, entity recognition, topic modeling) on unstructured data.
7. Query and analyze data from relational (PostgreSQL) and NoSQL (MongoDB, Elasticsearch) databases.
8. Collaborate with domain experts to translate business requirements into technical specifications.
**Required Skills**
* Advanced proficiency in Python and machine learning frameworks (TensorFlow, PyTorch, scikit‑learn).
* Experience with LLM frameworks: LangChain, LlamaIndex, VectorDBs.
* Cloud deployment on Azure, AWS, or GCP.
* NLP expertise: sentiment analysis, entity recognition, topic modeling, retrieval‑augmented generation.
* API design and microservices: FastAPI or Flask.
* SQL & NoSQL database querying (PostgreSQL, MongoDB, Elasticsearch).
* MLOps tools: MLflow, Kubeflow, Vertex AI.
* Strong verbal and written communication; ability to explain technical concepts to business stakeholders.
**Required Education & Certifications**
* Bachelor’s or advanced degree in Computer Science, AI, Data Science, Machine Learning, or related field.
* Preferred: cloud certifications (AWS Certified Machine Learning – Specialty, Azure AI Engineer Associate, GCP Professional Machine Learning Engineer).
* Additional certifications in NLP, MLOps, or GenAI are a plus.