[SXEIT-MM] 5 AIAgent Engineers

We are building the next generation of production AI agent systems systems that reason plan call tools delegate across specialized sub-agents and deliver real business outcomes end to end. We are looking for a senior engineer who has gone deep on modern machine learning transformers and agents and who wants to build at the frontier of what language models and multimodal systems can actually do in production today.

This is not a wrap an API around an LLM role. We expect candidates who understand why things work the mechanics of attention the failure modes of autoregressive decoding the cost/latency trade-offs of caching and tool routing the pitfalls of context engineering at scale. And who can turn that understanding into systems that ship hold up under load and keep improving.

Key Responsibilities:

• Architect agentic systems  plan memory tool use multi-agent delegation evaluation loops guardrails. Pick the right abstraction for the problem not the one on the hype curve.
• Push model capability into production. Design prompt and context strategies tool interfaces retrieval and reranking structured output streaming and evaluation across text and multimodal inputs (vision documents audio).
• Own the evaluation story. Build offline eval sets online LLM-as-judge loops regression harnesses. Know the difference between a metric that moves your users and a metric that moves only your dashboard.
• Squeeze the system. Prompt caching batching speculative decoding model routing token budget management latency targets. Know your P50/P99 and why they look the way they do.
• Contribute upstream. Read SDK source when docs are thin open PRs against open-source agent frameworks write crisp bug reports when a vendors orchestration service returns a weird 500.
• Mentor and set the bar. Your design reviews code reviews and technical writing shape how the rest of the team thinks about agents.

Qualifications :

• Bachelors degree in Artificial Intelligence Computer Science or a related field.
• 3 years of experience as an AI/AI Agent Engineer.
• Solid ML fundamentals. You can explain transformers attention positional encoding KV cache tokenisation sampling without hand-waving. Youve read at least a few core papers in full not just the abstracts.
• Deep LLM application experience. Multiple production systems built on top of frontier models (Anthropic OpenAI Gemini open-weight). You know the practical edge cases: tool-use stability structured-output failure modes long-context degradation prompt-injection defence cost control.
• Agent systems depth. Youve built something with real agent behaviour planning memory tool orchestration multi-step execution error recovery not a single prompt in a loop. Experience with multi-agent coordination (delegation sub-agent protocols MCP-style tool servers) is a strong plus.
• Multimodal experience. Hands-on work with vision-language models document AI (OCR layout tables) or audio end-to-end from ingest to grounded output.
• Strong engineering craft. Python at a senior level async typing testing packaging observability. Able to read and navigate a large codebase. Git hygiene that makes reviewers lives easier.
• Production AI engineering. You can take ML LLM embedding vision or multimodal models from prototype to production designing APIs and inference services building RAG/embedding pipelines containerizing workloads handling CPU/GPU deployment and optimizing latency throughput reliability and cost.
• AI platform & MLOps experience. Hands-on 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 or model-serving frameworks such as vLLM or NVIDIA Triton is a strong plus.
• Fluent with modern coding agents. You use Claude Code / Cursor / Copilot / equivalents daily as a force multiplier. You understand where they shine and where they fail and you can design prompts context and tool boundaries to get the most out of them.
• Communication. Writes and speaks clearly in English.

Strongly preferred

• Published ML work (papers workshop submissions open-source projects technical deep-dive blog posts) that shows original thinking.
• AI-native engineering mindset. Daily use of tools like Claude Code Cursor or Copilot with strong technical judgment
• Experience fine-tuning distilling or post-training open-weight LLMs SFT DPO RLHF LoRA even at small scale.
• Experience with vector search retrieval-augmented generation reranking at scale.
• Experience shipping production evaluation infrastructure (eval sets online judges A/B for LLM systems).

Additional Information :

WHY BOSCH

Because we dont just follow trends we create them.

Because together we turn ideas into reality working every day to make the world of tomorrow a better place.

Do you have high standards when it comes to your job So do we. At Bosch you will discover more than just work.

Benefits and Career Opportunities

• Working in one of the Best Places to Work in Vietnam and Top 30 of the Most Innovative Companies all over the world
• Join a dynamic and fast-growing global company (English-speaking environment)
• Engage in our diverse training programs which surely help strengthen both your personal and professionalism
• 13th-month salary bonus attractive performance bonus (youll love it!) annual performance appraisal
• 100% monthly salary and mandatory social insurances in 2-month probation
• Meal Commuting and Parking allowance
• Premium health insurance for you and 02 family members
• 15 days of annual leave  1 day of birthday leave
• Flexible working time
• Free in-house entertainment facilities and sport activities: football badminton yoga Aerobics.
• Vibrant culture: Enjoy team-building events company trips year-end parties tech talks charity & CSR initiatives
• Onsite opportunities: short-term and long-term assignments

Remote Work :

No

Employment Type :

Full-time