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AI Engineer

As an AI Engineer, you will be responsible for designing, building, and deploying intelligent systems that power our products and data-driven decision-making. You will work closely with engineering, product, and business teams to translate real-world problems into scalable AI solutions.
A. LLM & Agent Development
LLM Integration: Design and implement applications using state-of-the-art LLMs (e.g., OpenAI, Anthropic, open-source models), including prompt engineering, fine-tuning, and RAG pipelines.
Agent Systems: Build agent-based systems capable of multi-step reasoning, tool use, memory, and task orchestration.
Orchestration Frameworks: Work with agent frameworks (e.g., LangChain, LlamaIndex, CrewAI, AutoGen) and customize them for production use.
B. Evaluation, Reliability & Safety

LLM Evaluation: Design and maintain evaluation frameworks to measure accuracy, hallucination rates, latency, cost, and task success across LLM and agent workflows.

Automated Testing: Implement regression tests, golden datasets, and continuous evaluation pipelines for prompts, agents, and tools.

Reliability & Guardrails: Apply safety mechanisms such as output validation, structured generation, fallback strategies, and human-in-the-loop workflows.
C. Production Systems & MLOps

Deployment: Deploy LLM-powered services into production with scalability, observability, and cost control in mind.

Monitoring: Track real-world model behavior, failures, and drift; implement feedback loops for continuous improvement.

Optimization: Optimize inference latency, token usage, and infrastructure costs through caching, batching, and model selection strategies.
D. Research, Experimentation & Iteration

Applied Research: Stay current with advances in LLMs, agents, and evaluation techniques, and rapidly test new approaches.

Experimentation: Run structured experiments (A/B tests, prompt variants, agent policies) to validate improvements.

Prototyping: Quickly prototype new AI capabilities and harden successful experiments into production-ready systems.
E. Cross-Functional Collaboration

Product Alignment: Work closely with Product and Engineering teams to define AI-driven features aligned with user and business goals.

Technical Communication: Clearly explain system behavior, limitations, and trade-offs to both technical and non-technical stakeholders.

Documentation: Maintain high-quality documentation for prompts, agents, evaluation results, and system architecture.
Who You Are

You are an AI Engineer with deep hands-on experience in LLM-powered systems, excited about building reliable agents, not just demos. You care about evaluation, observability, and correctness, and understand that production AI requires rigor, not just clever prompts.

If you enjoy pushing the boundaries of LLMs while grounding them in measurable outcomes and robust engineering, we’d love to work with you.

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