AI Engineer (Applied)
Skills
About This Role
Overview
- BlackStone eIT is seeking a talented and pragmatic AI Engineer (Applied) to join our dynamic team.
- In this role, you will focus on applying artificial intelligence techniques to solve real-world problems and deliver impactful solutions that drive business value.
- You will collaborate closely with product teams, data scientists, and software engineers to deploy AI models into production environments and continuously improve their performance.
- The ideal candidate has hands-on experience with end-to-end AI system development and a strong focus on practical applications and scalability.
- You will work on diverse projects that require innovative AI solutions, from natural language processing to computer vision and predictive analytics.
- Data classification automation: implementing automated classification to remediate current failures, embedding classification into data pipelines alongside the Governance Lead
- Operational AI agents: building production agents on top of the agentic platform — going beyond the sample agents the external partner delivers into real operational workflows
- Agentic platform data contracts: defining what data the platform needs, in what format, with what quality guarantees — working with the Principal AI Engineer
- AI service implementation: FastAPI service around LLM APIs with versioned prompt templates
- Classification and briefing prompts: structured prompts returning validated JSON with tags, confidence levels, source attribution
- Prompt versioning: templates in configuration, editable without code changes
- Observability: every LLM call logged with input hash, model version, output, latency, token count
- Fallback logic: graceful degradation when LLM APIs are unavailable
- Quality evaluation: running precision/recall evaluations against human reviewer samples, reporting results, iterating prompts
Requirements
- 5+ years of experience applying AI and machine learning techniques in a production environment.
- Strong proficiency in programming languages such as Python and familiarity with AI/ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
- Experience with deploying and maintaining AI models at scale.
- Good understanding of data preprocessing, feature engineering, and model evaluation.
- Background in statistics, mathematics, or computer science.
- 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.
- Experience with cloud platforms (AWS, GCP, Azure) and MLOps practices is a plus.
- Bachelor’s or Master’s degree in a relevant field such as Computer Science, Data Science, or AI.
- + LLM APIs (Claude, GPT-4, open-weight models) — structured output, JSON mode, system prompts
- + Prompt engineering for classification — zero-shot and few-shot
- + Python — async API calls, retry logic, exponential backoff
- + LLM evaluation — precision/recall, human-AI agreement scoring
- + Structured output — JSON schema enforcement, Pydantic validation
- + Open-weight / sovereign model APIs (Falcon, Llama, or equivalent)
- + Token budgeting and context window management
- + AI observability — output quality monitoring, anomaly detection
- + FastAPI and Docker
• Paid Time Off
- Performance Bonus
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