AI Engineer

• Design, build, and evolve agentic AI systems that reason, plan, execute, and adapt in production environments.
• Take AI-driven features from concept to production in a true 0–1 product environment.
• Write and review high-quality production code (Python-first) across AI pipelines, inference services, orchestration layers, and supporting systems.
• Implement prompt engineering, tool use, memory, evaluation, and guardrails as first-class engineering concerns, not experiments.
• Design agent frameworks that balance autonomy with determinism, observability, and safety.
• Make pragmatic architectural trade-offs across latency, cost, accuracy, scalability, and maintainability.
• Integrate and operate LLMs (commercial and/or open-source) including model selection, fine-tuning strategies, embeddings, retrieval (RAG), and inference optimization.
• Address real-world issues: hallucinations, drift, prompt regressions, failure modes, and customer trust.
• Deploy and operate AI services across cloud platforms (AWS, Azure, GCP), including secure enterprise integrations and customer-specific deployments.
• Design scalable inference and orchestration architectures using containers, APIs, and distributed systems.
• Ensure the platform is shippable, debuggable, and supportable — not fragile or research-grade.
• Act with founder-level ownership: identify gaps, propose solutions, and move forward without waiting for perfect requirements.

To ensure you’re set up for success, you will bring the following skillset & experience:

• 10+ years of professional software development experience, with significant time shipping B2B products used by external customers.
• Strong software engineering foundation with expert-level Python and experience designing production systems.
• Proven experience building, deploying, and operating AI-powered products in production — not just prototypes or research.
• Hands-on experience with LLMs and GenAI systems in real applications (e.g., agents, copilots, automation, decision systems).

Deep understanding of at least several of the following:

• Agent frameworks and orchestration
• Prompt engineering and tool-use patterns
• RAG architectures and vector search
• Model evaluation, feedback loops, and monitoring
• Safety, guardrails, and enterprise controls

 Hands-on experience with multiple of the following in real systems:

• LangGraph and/or LangChain
• LlamaIndex
• Vector databases (e.g., Pinecone, Weaviate, FAISS, Milvus)
• Prompt engineering as a managed, versioned, testable artifact
• Experience deploying and operating LLMs using: -AWS SageMaker, Vertex AI, or equivalent managed platforms. Direct API integrations (OpenAI, Anthropic)
• Experience designing multi-agent systems or complex agent workflows.
• Experience commercializing AI features under enterprise constraints (security, compliance, uptime).
• Comfort operating in ambiguity and making decisions with incomplete information.

Whilst these are nice to have, our team can help you develop in the following skills:

• Contributions to open-source GenAI tooling or internal frameworks used at scale
• Experience with Supervised fine-tuning, Parameter-efficient tuning methods (LoRA, QLoRA), reinforcement learning (RLHF) and preference optimization (PPO, DPO, GRPO).
  • Experience deploying LLMs at scale (Kubernetes, model serving, GPU optimization).