AI Engineer

Role Overview

We are seeking an AI Engineer to create, build, and deploy dependable AI-driven applications and intelligent workflows. The main focus is developing solutions using Large Language Models (LLM) and generative AI technologies such as retrieval-augmented generation, agent workflows, AI system integrations, and supporting data pipeline architecture.

Key Responsibilities

• Develop AI applications by programming Python-based services, APIs, and workflows utilizing model APIs, structured outputs, and tool invocations.
• Integrate AI systems with existing databases and enterprise business applications.
• Collaborate with users to gather requirements and iteratively enhance solutions based on feedback.
• Engineer effective AI context by implementing document ingestion, chunking, embeddings, retrieval mechanisms, and context compilation.
• Enhance relevance and timeliness of source data and citations while managing instructions, conversational history, and token usage limits.
• Apply appropriate data-access protocols and security policies during information retrieval.
• Design agent workflows involving tool schemas, execution sequencing, state management, retry policies, timeout mechanisms, and comprehensive logging.
• Incorporate validation and human approval checkpoints when necessary.
• Test reliability and ensure recovery strategies for failed tool executions.
• Create representative test datasets and build automated validation checks.
• Analyze system traces to diagnose issues related to retrieval, models, and tool interactions.
• Assess updates by measuring task success rates, answer quality, system latency, and operational cost.
• Document system constraints, issues encountered, and performance regressions.
• Develop data foundation code using SQL and data processing to ingest, sanitize, and transform both structured and unstructured data.
• Validate data schemas, identify missing values, remove duplicates, and ensure data freshness.
• Maintain robustness of data pipelines, indexes, and retrieval components.
• Employ AI-assisted coding tools and agents responsibly to speed up development and debugging.
• Produce clear repository instructions and preserve task context for collaborators.
• Review AI-generated code critically, conduct meaningful tests, and provide explanations for implementation decisions.
• Contribute well-tested code through Git workflows and code review procedures.
• Manage the packaging, deployment, monitoring, and maintenance activities for AI applications.
• Investigate software defects and maintain technical documentation.
• Adhere to security standards including access control, secrets management, and data protection.

Required Qualifications and Experience

• A university degree or equivalent qualification in Computer Science, Artificial Intelligence, Data Science, Engineering, Mathematics, or related fields.
• A minimum of two years’ experience in AI engineering, software development, data engineering, machine learning, or related technical disciplines.
• Proficiency in Python programming and SQL, including experience with APIs, JSON data formats, version control systems like Git, debugging techniques, and automated testing frameworks.
• Hands-on experience building and deploying AI or LLM applications, including at least one project integrating retrieval systems, databases, APIs, or tooling.
• Solid understanding of core LLM concepts such as tokenization, context window size, embeddings, retrieval augmented generation, structured outputs, and tool execution.
• Knowledge of data and machine learning principles: data preparation processes, training versus inference distinctions, validation methods, overfitting and data leakage challenges, neural network architectures, and transformer models.
• Proven ability to interpret business needs and translate them into effective technical solutions, collaborating closely with users and stakeholders.
• Strong analytical and engineering problem-solving skills, capable of troubleshooting, testing, explaining technical choices, and adapting rapidly to evolving technologies and tools.
• Familiarity with AI agent frameworks, vector databases, cloud computing platforms, or machine learning operations (MLOps) is a plus.