AI Engineer (Applied)

About the role

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

Responsibilities

• - 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

Skills

• Python
• TensorFlow
• PyTorch
• Scikit-learn
• FastAPI
• Docker
• LLM APIs (Claude, GPT-4, open-weight models)
• Prompt engineering (classification, zero-shot, few-shot)
• JSON schema validation
• Pydantic validation
• AI observability
• Latency and performance monitoring
• MLOps
• Open-weight / sovereign model APIs (Falcon, Llama)