[SX/EIT-MM] 5 AI/Agent Engineers

About the company

The Bosch Group is a leading global supplier of technology and services. Since the beginning of 2013, its operations have been divided into four business sectors: Automotive Technology, Industrial Technology, Consumer Goods, and Energy and Building Technology.

The Bosch Group comprises Robert Bosch GmbH and its roughly 360 subsidiaries and regional companies in some 50 countries. If its sales and service partners are included, then Bosch is represented in roughly 150 countries. This worldwide development, manufacturing, and sales network is the foundation for further growth.

Bosch Global Software Technologies Company Limited (BGSV) is 100% owned subsidiary of Robert Bosch GmbH - one of the world’s leading global suppliers of technology and services, offering end-to-end Engineering, IT, and Business Solutions.

Starting its operation from 2010 at Etown 2 in HCMC, BGSV is the first software development center of Bosch in Southeast Asia. BGSV nowadays have over 4,000 associates, with a global footprint and presence in the US, Europe, and the Asia Pacific region.

With our unique ability to offer end-to-end solutions that connect sensors, software, and services, we enable businesses to move from the traditional to digital or improve businesses by introducing a digital element in their products and processes.

Full job description

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 vendor's 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 and requirements

• Bachelor’s 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. You've 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. You've 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 don't 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 (you'll 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

• Bachelor’s 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. You've 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. You've 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).

Discovered from the employer’s public SmartRecruiters Posting API. The public detail endpoint was fetched and its job-ad sections were normalized into a complete, safe candidate-facing description.