About the Role
We’re looking for an AI Engineer with solid fundamentals in Python,
PyTorch, and LLMs, and at least 3 years of experience in NLP/ML or
equivalent. You will work closely with engineering and product teams to
develop end-to-end LLM systems, orchestrate agent pipelines, and deliver
production features. You should be comfortable with model training,
prompt engineering, and taking ownership of AI workflows. This role is
ideal for someone eager to advance LLM-based agents and contribute to
scalable AI products.
Responsibilities
- Responsible for end-to-end development, deployment, and continuous improvement of advancedLLM systems - including dataset curation, labeling strategy, model architecture, training, and rigorous evaluation.
- Research, design, and implement cloud-based AI models, optimizing for low latency, privacy, and resilience in both connected and constrained environments.
- Drive complex orchestration of LLM workflows and agent pipelines, blending prompt engineering, and context grounding for automation.
- Collaborate with engineering and product teams to define success metrics, design and execute experiments (e.g., A/B testing), monitor user signals, and iterate on deployed features.
- Advance the state-of-the-art through applied research, prototyping new concepts in LLM-based agents, and translating breakthroughs into scalable, user-centric product features.
- Work closely with AI and company leadership, foster a collaborative and inclusive culture, and contribute to team talent development.
- Stay current with research and industry trends, identifying opportunities to integrate emerging technologies and methods into the LLM AI ecosystem.
Required Qualifications
- Master's Degree in Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., NLP, statistics predictive analytics, research)
OR equivalent experience. - 2+ years of experience in coding in Python and modern ML frameworks such as PyTorch (or equivalent).
- 1+ year of experience in working with LLM (fine-tuning knowledge is a plus), Prompt Engineering
Preferred Qualifications - Hands-on experience orchestrating LLM-based agent pipelines - prompt design, tool invocation, and context grounding - for productivity scenarios, alongside other advanced ML techniques.
- Proven expertise and background in designing and implementing AI models that serve thousands of users in scaled production environments.
- Demonstrated success moving LLM into shipped product features, with clear ownership of the end-to-end ML lifecycle - data collection, cleaning, modeling, evaluation, and continuous improvement.
- Experience in designing, evaluating, and iterating on LLM or agent-based workflows for real-world productivity applications.
- Deep understanding of transformer-based architectures and hands-on experience with model optimization techniques (quantization, adapter tuning).
- Strong leadership and cross-functional influence skills; ability to attract and develop talent while fostering a collaborative team environment.
- Customer-obsessed mentality and passion for translating research breakthroughs into meaningful product impact.