AI Engineer (Applied)

Role Overview

AI Engineer (Applied) at BlackStone eIT. This is a hands-on role focused on applying artificial intelligence techniques to solve real-world problems and deliver impactful solutions that drive business value.

Role Purpose

Deploy AI models into production environments, collaborate with product teams, data scientists, and software engineers to continuously improve performance, and work on diverse projects requiring innovative AI solutions from natural language processing to computer vision and predictive analytics.

Key Responsibilities

Data Classification & Automation

• Implement automated data classification to remediate current failures.
• Embed classification into data pipelines alongside the Governance Lead.

Operational AI Agents

• Build production agents on top of the agentic platform.
• Develop beyond sample agents delivered by external partners into real operational workflows.

Agentic Platform & Data Contracts

• Define what data the platform needs, in what format, with what quality guarantees.
• Work with the Principal AI Engineer on data contract specification.

AI Service Implementation

• Develop FastAPI service around LLM APIs with versioned prompt templates.
• Implement classification and briefing prompts returning structured, validated JSON with tags, confidence levels, and source attribution.
• Enable prompt versioning using templates in configuration, editable without code changes.

Observability & Monitoring

• Log every LLM call with input hash, model version, output, latency, and token count.
• Implement graceful degradation and fallback logic when LLM APIs are unavailable.

Quality Evaluation

• Run precision and recall evaluations against human reviewer samples.
• Report results and iterate on prompts to improve performance.

Qualifications & Experience

• 5+ years of experience applying AI and machine learning techniques in a production environment.
• Bachelor's or Master's degree in Computer Science, Data Science, AI, or a relevant field.
• Background in statistics, mathematics, or computer science.
• Experience with deploying and maintaining AI models at scale.
• Good understanding of data preprocessing, feature engineering, and model evaluation.

Skills & Competencies

• Strong proficiency in Python, including async API calls, retry logic, and exponential backoff.
• Proficiency with AI/ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Experience with LLM APIs including Claude, GPT-4, and open-weight models.
• Knowledge of structured output formats, JSON mode, JSON schema enforcement, and Pydantic validation.
• Prompt engineering expertise for classification tasks, including zero-shot and few-shot techniques.
• LLM evaluation capabilities including precision/recall and human-AI agreement scoring.
• FastAPI and Docker experience.
• Token budgeting and context window management.
• AI observability practices including output quality monitoring and anomaly detection.
• Familiarity with open-weight and sovereign model APIs such as Falcon, Llama, or equivalent.
• Experience with cloud platforms (AWS, GCP, Azure) and MLOps practices.
• Ability to collaborate effectively with cross-functional teams and translate business needs into applied AI solutions.
• Excellent problem-solving skills and a practical, solution-oriented mindset.

Additional Information

• Paid Time Off.
• Performance Bonus.
• Training & Development.