ASW-Enterprise AI Engineer

Role & responsibilities

As an Enterprise AI Engineer, you will contribute to the design, development, and validation of Natural Language Processing (NLP) and Large Language Model (LLM) applications targeting enterprise use cases. You will apply state-of-the-art techniques to address complex business problems in areas such as data analysis, document processing, task management, visualization, and information retrieval. The ideal candidate will have proven experience building practical, robust, scalable, efficient, and cost-effective AI solutions using state-of-the-art LLMs, and will play a key role in the integration of organization data with these models.

Preferred candidate profile

Mandatory Skills Required (specify keywords)

• Llama
• Retrieval Augmented Generation (RAG)
• LangChain
• LlamaIndex
• Vector Database
• Vector DB
• pgvector
• Python

Good to have skills (Preferred)

1. Expertise in NLP and LLM such as Hugging Face, OpenAI GPT, BERT, or similar models.
2. Experience in development of Chatbots and/or Virtual Assistants.
3. Experience with fine-tuning LLMs for domain-specific applications or industry verticals (e.g., automotive, industrial, etc.).
4. Proven experience developing and deploying custom LLMs with low computational footprints, including using techniques like knowledge distillation, model pruning, or quantization.
5. Knowledge of distributed computing, containerization (Docker, Kubernetes), and scaling AI solutions in enterprise environments.
6. Certifications in AI or machine learning (e.g., AWS Certified Machine Learning, Google Professional Machine Learning Engineer).
7. Familiarity with cloud platforms (AWS, Azure) for deploying AI solutions is a plus.

Competencies

1. Technical expertise in AI/ML, data engineering, cloud computing, and enterprise system integration.
2. Problem-solving and analytical thinking to develop AI solutions for complex business challenges.
3. Communication skills to articulate complex ideas clearly to both technical and non-technical stakeholders.
4. Project management skills for planning, executing, and delivering AI projects.
5. Collaboration and teamwork to work with cross-functional teams.
6. Continuous learning and adaptability in a rapidly evolving technological environment.