- Company Name
- Space Executive
- Job Title
- Artificial Intelligence Engineer
- Job Description
-
Job Title: Artificial Intelligence Engineer
Role Summary:
Design, develop, and deploy enterprise‑grade AI agents that integrate large language models, retrieval‑augmented generation, and agentic workflows. Leverage data science, machine learning, and MLOps expertise to transform complex business challenges into scalable AI solutions for high‑value clients.
Expectations:
- Deep understanding of machine learning, data science, and modern AI technologies (LLMs, RAG, LangChain, co‑pilot frameworks, agentic systems).
- Proven ability to translate stakeholder requirements into technical AI architectures.
- Continuous learning mindset to keep pace with rapidly evolving AI tools and practices.
- Strong communication skills to collaborate with cross‑functional teams and executive stakeholders.
Key Responsibilities:
- Architect and implement custom AI agents tailored to enterprise client needs.
- Integrate LLMs, RAG pipelines, and agentic workflows with existing data infrastructure.
- Design end‑to‑end ML pipelines, including data preprocessing, model training, evaluation, and deployment.
- Ensure model performance, scalability, and robustness in production environments.
- Conduct technical workshops and demos to explain AI solutions to business users.
- Participate in MLOps practices: CI/CD, monitoring, and continuous improvement of models.
- Collaborate with data engineers, product managers, and domain experts to refine AI use cases.
Required Skills:
- Programming: Python, libraries (PyTorch/TensorFlow, Hugging Face, LangChain, Ray, etc.).
- Machine Learning: supervised/unsupervised learning, model evaluation, hyperparameter tuning.
- AI Platforms: OpenAI, Anthropic, Azure OpenAI, Google Vertex AI or equivalent.
- Retrieval‑Augmented Generation and RAG pipelines.
- Agentic workflows and co‑pilot integration.
- MLOps: Docker, Kubernetes, CI/CD, monitoring tools.
- Data handling: SQL, NoSQL, data pipelines (Airflow, Prefect).
- Stakeholder communication and requirement elicitation.
- Problem‑solving, analytical thinking, and rapid prototyping.
Required Education & Certifications:
- Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
- Professional certifications (e.g., Google Cloud Professional ML Engineer, AWS Certified Machine Learning – Specialty) are a plus.