Sr. AI Engineer
In this role, you will help design and deliver next-generation AI systems that combine machine learning, large language models, and agentic workflows to solve complex, real-world business problems. You will be responsible for building production-grade AI applications that are both innovative and rigorously evaluated for quality, safety, and performance. Working in a highly collaborative environment, you will partner closely with product, engineering, and business stakeholders to translate ambiguous needs into scalable AI solutions. A strong emphasis is placed on evaluation-first design, ensuring that every system has measurable success criteria from the outset. You will also contribute to strengthening MLOps and LLMOps practices, improving how AI is deployed, monitored, and continuously optimized. This is a hands-on, high-impact role where technical depth, curiosity, and accountability directly shape production systems used at scale.
Accountabilities:
Design, build, and deploy AI solutions using machine learning models, large language models, and agentic AI systems to solve real business challenges.
Define evaluation strategies upfront, including success metrics, offline and online testing frameworks, error analysis, and production monitoring plans.
Develop and optimize LLM-based applications using prompt engineering, retrieval-augmented generation, and multi-step reasoning workflows.
Apply MLOps and LLMOps best practices, including versioning, observability, alerting, continuous evaluation, and iterative model improvement.
Collaborate with cross-functional teams to prioritize AI use cases, align on success criteria, and ensure scalable and reliable delivery.
Requirements:
Bachelor's degree in Computer Science or related field, with 6+ years of relevant experience in machine learning and modern AI systems.
4+ years of hands-on experience in machine learning, including model development, training, evaluation, and error analysis.
2+ years of experience building LLM-based applications, including at least 1 year working on agentic AI systems.
Strong expertise in applied ML or NLP, including experimentation, model evaluation, and iterative improvement.
Experience building LLM applications with retrieval systems, context engineering, evaluation frameworks, and fine-tuning approaches.
Proven experience designing agentic systems with multi-step workflows, tool use, memory, and human-in-the-loop processes.
Strong MLOps experience covering deployment, monitoring, retraining, and production performance tracking.
Strong LLMOps experience including prompt/version management, observability, guardrails, and continuous optimization.
Advanced Python skills and experience delivering production-grade AI systems balancing latency, cost, scalability, and maintainability.
Evaluation-first mindset with humility, adaptability, and strong collaboration skills with technical and business stakeholders.
Benefits:
Competitive compensation aligned with experience and market standards
Flexible work environment supporting remote and hybrid arrangements
Comprehensive healthcare coverage options depending on location
Retirement and pension plan contributions with stock participation opportunities
Life and disability insurance coverage for income protection
Generous paid time off, holidays, and volunteer days
Learning and development support including e-learning access, tuition reimbursement, and hackathons
Home office setup allowance to support remote productivity
Optional benefits such as pet insurance, legal assistance, and identity theft protection