Artificial Intelligence Engineer

The AI Engineer is the technical core of an AI-native consulting and engineering team, focused on delivering enterprise-scale AI transformations. This role goes far beyond prompt engineering—owning the design, build, and operation of production-grade AI systems, including large language models (LLMs), multi-agent architectures, retrieval pipelines, and AI-enabled execution workflows.

The engineer translates ambiguous business needs into safe, reliable, and measurable AI solutions while collaborating closely with consulting and orchestration teams.

Key Responsibilities:

AI Engineering & Agentic System Design

• Design and implement production-grade LLM applications using modern orchestration patterns, prompt frameworks, and evaluation loops.
• Build and maintain multi-agent architectures, including planning, delegation, tool use, safety constraints, and monitoring.
• Develop retrieval and vector-based systems, embeddings, semantic workflows, and structured reasoning pipelines.
• Apply ontology and knowledge modeling literacy to support precise reasoning and data alignment.
• Integrate AI systems with APIs, tools, and enterprise connectors.
• Develop scalable prompt engineering solutions, including pattern libraries, guardrails, and explainability checks.
• Establish technical direction for projects and architectural foundations for client solutions.

Technical Discovery & Solution Architecture

• Translate vague or ambiguous business requirements into clear engineering approaches.
• Conduct feasibility assessments across data, systems, and architecture.
• Contribute to use-case shaping and value-framing by providing technical clarity.
• Collaborate with domain experts to understand edge cases and operational requirements.
• Produce lightweight technical documentation, diagrams, and decision records.

Deployment, Quality, AI Ops & Reliability

• Implement evaluation frameworks and safety checks for models and agent behavior.
• Build monitoring pipelines, logs, traces, and incident-handling protocols.
• Apply governance, risk, and compliance principles in AI deployments.
• Support release cycles, environments, and operational readiness.
• Ensure reliability, reproducibility, and performance benchmarks are met.

Internal Knowledge & Capability Building

• Develop reusable accelerators, agent patterns, and technical templates.
• Contribute to internal engineering curriculum and capability-building initiatives.
• Share emerging research, frameworks, and technical insights with the team.
• Influence coding standards, architectural patterns, and methodologies.

Required Experience & Skills:

• 4–8 years in AI engineering, ML engineering, software engineering, or applied data engineering.
• Strong hands-on experience with:
• Embeddings and vector stores
• Python proficiency at production level.
• Demonstrated experience deploying AI systems—not just prototypes.
• Comfortable in client-facing delivery alongside consultants and orchestrators.

Very Strong Indicators:

• Multi-agent system design and orchestration (planning, delegation, tool integration).
• Evaluation frameworks, safety checks, and monitoring pipelines.
• AI Ops / observability experience.
• Familiarity with ontologies or semantic reasoning (literacy required).

Critical Distinction:

• Candidates must be able to explain architectural trade-offs, discuss failure modes, demonstrate evaluation and reliability practices, and show ownership of systems in production.
• Shallow “ChatGPT experience” alone will not meet the standard.

Attributes:

• AI-native, deeply immersed in AI tooling, research, and emerging engineering patterns.
• High agency, self-motivated, entrepreneurial, and action-oriented.
• Structured problem-solving in ambiguous environments using first-principles reasoning.
• Builder mindset: rapid prototyping, iterative development, and solution refinement.
• Clear communicator capable of simplifying complex systems for clients and colleagues.
• Collaborative, low ego, and thrives in small senior teams.
• Quality-driven, with a focus on safety, reliability, and trust in AI systems.

Interview Flow:

• Initial technical discussion with CTO
• Second-stage interview with senior engineering leadership

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