The Senior Agentic AI Engineer will lead the development of advanced generative AI solutions at Trimble, focusing on building LLM-powered autonomous agents that connect digital and physical logistics systems. This role is central to transforming global freight movement by designing and deploying scalable, intelligent AI systems that enhance safety, efficiency, and sustainability in transportation and logistics.
Responsibilities
- Architect and code sophisticated AI modules, including high-performance embedding pipelines, custom chunkers, and advanced retrieval evaluators.
- Lead the integration of intelligent agent services with diverse internal and external APIs and vector databases to solve complex, cross-service challenges.
- Stay at the bleeding edge of AI research, implementing the latest advancements in fine-tuning, RLHF, and prompt engineering frameworks like DSPy.
- Drive agile excellence by spearheading sprint deliveries and collaborating with cross-functional leaders to define the scope of transformative AI features.
- Champion code quality and team growth by performing high-level peer reviews and contributing to shared utility libraries that boost development velocity.
- Architect and implement modular, cloud-native microservices that integrate GenAI into our production ecosystem, ensuring our Agentic solutions are as scalable and reliable as they are intelligent.
- Take ownership of the production lifecycle by building robust CI/CD pipelines and using observability frameworks (like Datadog or Databricks) to monitor model health, trace agent logic, and ensure system uptime.
- Drive technical excellence by developing comprehensive evaluation suites—incorporating both traditional testing and modern LLM-based evals—to benchmark accuracy, safety, and performance before every release.
Requirements
- 6+ years of robust engineering experience
- Python expert with a deep understanding of developing and scaling high-quality code
- At least 6 months of hands-on experience building and deploying GenAI applications
- Strong background in RAG frameworks, including vector databases, hybrid search, and ANN algorithms
- B.Tech, M.E, M.Tech, or M.S. in Computer Science Engineering from a premier institute
Preferred Qualifications
- Recognition in national-level Olympiads or talent search examinations
- A history of contributing to open-source projects or presenting technical papers at national forums
- GenAI Passion: portfolio of personal projects, technical blog, or GitHub repository demonstrating hands-on experimentation with latest LLM frameworks
- Community Contribution: contributed to open-source AI libraries (like LangChain, LlamaIndex, or AutoGPT) or shared insights at technical meetups
Technical Stack
Python, LLM, RAG frameworks, vector databases, hybrid search, ANN algorithms, DSPy, LangChain, LlamaIndex, AutoGPT, Datadog, Databricks, CI/CD pipelines, cloud-native microservices, APIs
Benefits
- Work on projects that truly matter with tangible real-world impact
- Collaborate with a collaborative, supportive team that shares your purpose
- Foster a genuine sense of belonging in a strong internal culture
- Be part of a 'visionary pragmatist' team that thinks boldly and builds things that work
- Grow your career in a purpose-driven culture
- Innovate in an environment where your ideas shape the future
- Empowerment to create impactful strategies within local teams while contributing to a global vision
