ABOUT THE JOB

• Architect and build advanced AI agentic systems end-to-end — including planning, memory, tool use, multi-agent delegation, evaluation loops, and guardrails — always choosing the right abstraction for the real problem, not just what’s trending
• Design and implement LLM-powered applications in production, defining prompt and context strategies, tool interfaces, retrieval and reranking logic, structured outputs, streaming, and evaluation — across text and multimodal inputs (vision, documents, audio)
• Own the evaluation strategy for AI/agent systems: build offline evaluation datasets, design online LLM-as-judge loops, and set up regression harnesses that focus on metrics that truly impact users, not just dashboards
• Optimize and “squeeze” AI systems for performance and cost: implement prompt caching, batching, speculative decoding, model routing, token budget management, and latency targets; monitor and understand P50/P99 behaviour and continuously improve it
• Lead productionization of AI workloads: design APIs and inference services, build RAG/embedding pipelines, containerize workloads, and handle CPU/GPU deployment while optimizing latency, throughput, reliability, and cost
• Contribute upstream to the AI ecosystem: read SDK source code when documentation is limited, open PRs to open-source agent frameworks, and write clear bug reports for vendors when orchestration services misbehave
• Operate AI systems in production with strong AI platform & MLOps practices — model/version management, CI/CD, evaluation gates, observability, autoscaling, rollback, and failure handling, using tools such as Docker, Kubernetes/AKS, Azure AI services, vLLM or NVIDIA Triton
• Work hands-on with multimodal models (vision-language, document AI, audio) from ingest to grounded output, including OCR, layout understanding, tables, and speech processing
• Collaborate closely with Product Owners, engineers, and stakeholders to understand business needs and translate them into robust agent architectures, LLM workflows, and technical solutions
• Mentor other engineers and set the technical bar through design reviews, code reviews, and technical writing that shape how the team thinks about agents and AI systems

ABOUT YOU

• Bachelor’s degree in Artificial Intelligence, Computer Science, or a related field — or equivalent practical experience
• 1+ years of experience as an AI / AI Agent Engineer, working on real-world AI or LLM-based applications
• Solid machine learning fundamentals: able to clearly explain transformers (attention, positional encoding, KV cache, tokenisation, sampling) and familiar with core research papers beyond just the abstracts
• Deep LLM application experience: you have built multiple production systems on top of frontier models (e.g., Anthropic, OpenAI, Gemini, open-weight models) and understand practical edge cases such as tool-use stability, structured-output failure modes, long-context degradation, prompt-injection defence, and cost control
• Proven agent systems depth: experience building systems with real agent behaviour — planning, memory, tool orchestration, multi-step execution, and error recovery — beyond simple single-prompt loops; multi-agent coordination (delegation, sub-agent protocols, MCP-style tool servers) is a strong plus
• Hands-on multimodal experience with vision-language models, document AI (OCR, layout, tables), or audio, including end-to-end pipelines from data ingest to grounded outputs
• Strong engineering craft with Python at a senior level — async programming, typing, testing, packaging, observability — and the ability to navigate and contribute to large codebases with clean Git practices
• Solid production AI engineering background: taking ML, LLM, embedding, vision, or multimodal models from prototype to production, designing APIs and inference services, building RAG/embedding pipelines, and deploying/optimizing workloads on CPU/GPU
• Practical AI platform & MLOps experience: operating AI workloads in production with model/version management, CI/CD, evaluation gates, observability, autoscaling, rollback, and failure handling; experience with Docker, Kubernetes/AKS, Azure AI services, GPU inference, vLLM, or NVIDIA Triton is a strong plus
• Fluent with modern coding agents (Claude Code, Cursor, Copilot, or equivalents) and able to design prompts, context windows, and tool boundaries to use them effectively while understanding their limitations
• Fluent English communication skills (spoken and written) — able to explain complex technical concepts clearly and collaborate with international stakeholders
• You are curious, rigorous, and proactive — comfortable working at the frontier of AI, collaborating with both technical and non-technical team members, and continuously raising the bar for agentic systems and AI engineering

WHY AMARIS?

• Competitive salary and 13th-month salary
• 14+ annual leave days per year
• Premium healthcare insurance starting from your probation period
• Regular project reviews and yearly performance appraisal
• Annual company trip
• Team-building activities: team lunch/dinner, events and celebrations, sports clubs (football, basketball, badminton, pickleball)
• International working environment
• Tailor-made career path and clear growth opportunities
• Technical workshops and training courses (internal & external)
• Mobility opportunities to work on-site in our offices in 60+ countries

Equal Opportunity

Amaris Consulting is proud to be an equal opportunity workplace. We are committed to promote diversity within the workforce and creating an inclusive working environment. For this purpose, we welcome applications from all qualified candidates regardless of gender, sexual orientation, race, ethnicity, beliefs, age, marital status, disability, or other characteristics.

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