Highlights
Work with cutting-edge technology, impact product development directly, collaborate closely with researchers.
Description
Job Summary
pSynthBee is seeking a Machine Learning Engineer to develop AI-driven reasoning agents and frameworks for automating complex tasks. This role involves building and optimizing RAG pipelines, distilling and fine-tuning models, and deploying them into production systems using Python and cloud infrastructure.
Responsibilities
- Develop AI-driven reasoning agents and frameworks for automation
- Build and optimize Retrieval-Augmented Generation (RAG) pipelines
- Distill and fine-tune AI models to improve automation
- Deploy AI models into scalable production systems using Python and cloud tools
- Collaborate with researchers to operationalize advancements in real applications
Required Skills
- Agentic AI frameworks (e.g., AutoGen, LangChain)
- AI-powered automation workflows
- Vector databases and retrieval systems (LlamaIndex, FAISS)
- Fine-tuning AI models with Python
- Cloud deployment tools (AWS/GCP/Azure)
Required Skills Explained
- Proven experience building and deploying AI applications, especially in agentic frameworks like AutoGen or LangChain.
- Strong background in AI-powered automation and orchestration workflows.
- Experience with vector databases and retrieval systems such as LlamaIndex or FAISS.
- Solid coding skills in Python, essential for developing and deploying machine learning models.
- Familiarity with cloud deployment tools like AWS, GCP, or Azure to ensure scalable production environments.
Who is this for
pThis role suits a generalist who has experience building and deploying AI applications, especially in fast-moving environments. You should be excited about agentic AI and solving real-world problems through autonomous workflows.
Why This Job is a Good Opportunity
ulliJoin a team that's at the forefront of building Collaborative Intelligence (CI), a revolutionary approach to combining human and AI efforts for significant scientific and engineering challenges.liWork in a dynamic, fast-paced environment where your work directly impacts the product and helps drive innovation.liCollaborate closely with researchers to turn cutting-edge research into practical applications that solve real-world problems.liThe opportunity to work on complex AI-driven reasoning agents that automate tasks in innovative ways.
Interview Preparation Tips
- Highlight your experience with building and deploying AI applications, especially in agentic frameworks like AutoGen or LangChain.
- Prepare examples of how you have worked with vector databases and retrieval systems to optimize performance.
- Discuss your familiarity with cloud deployment tools and how they can be leveraged for scalability.
- Be ready to explain specific projects or side hustles where you've demonstrated strong coding skills in Python.
Career Growth in This Role
pThis role offers numerous opportunities for career growth, including specialization in agentic AI systems, leadership positions within the engineering team, and advancements into research-to-production roles. The fast-paced startup environment fosters quick learning and development of new skills, making it ideal for those looking to advance their careers in machine learning.pMoreover, as SynthBee continues to expand its CI offerings, professionals in this role can explore diverse career paths, such as leading AI-driven product teams or contributing to the development of new AI technologies. The company's focus on innovation and human-AI collaboration ensures a continuous stream of challenging and rewarding projects.
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Skills
Frequently Asked Questions
What kind of experience is required for this role?Proven experience building and deploying AI applications, especially in startups or fast-moving environments.
Can I still apply if I don't have extensive fine-tuning experience?Yes, some experience with fine-tuning is a plus but not required. We value generalists who can quickly learn and adapt.
What technologies do you use for cloud deployment?We primarily use AWS, GCP, and Azure for deploying our AI models into production systems.
