Why Join Hirevector?
At Hirevector, we are redefining the future of health‑focused retail through cutting‑edge technology. Founded over a century ago, our legacy is built on community service, accessibility, and innovation. Today, with a network of thousands of physical locations and a thriving digital ecosystem, we empower millions of customers to lead healthier lives. Our AI‑driven initiatives, especially in conversational interfaces, are at the heart of this transformation. As a Machine Learning Operations (MLOps) Engineer – Conversational AI, you will play a pivotal role in translating advanced NLP research into reliable, scalable products that touch the daily lives of our users.
About the Role
The position is a full‑time, remote opportunity based in California. You will be part of a multidisciplinary AI team that designs, builds, and maintains end‑to‑end pipelines for voice and text‑based natural language processing (NLP) models. Your work will ensure that our AI solutions are production‑ready, performant, and continuously improving, while adhering to the highest standards of data privacy and security.
Key Responsibilities
- Drive the coordination and democratization of innovative, data‑centric solutions across multiple business units.
- Design, implement, and optimize MLOps pipelines for deploying NLP and voice models on cloud platforms.
- Develop and maintain robust data ingestion, transformation, and validation workflows that feed model training and inference.
- Collaborate with data scientists, software engineers, and product owners to translate research prototypes into production‑grade services.
- Monitor model performance, conduct A/B testing, and iterate on models to improve accuracy, latency, and throughput.
- Maintain detailed metadata and version control for all models, ensuring reproducibility and compliance with governance policies.
- Implement automated CI/CD processes, testing suites, and quality‑assurance mechanisms for AI artifacts.
- Stay abreast of the latest advancements in AI, NLP, voice processing, and cloud technologies, and share insights with the team.
- Contribute to the Center of Excellence by documenting best practices, creating training materials, and mentoring junior engineers.
- Travel up to 10 % of the time for on‑site collaboration with cross‑functional teams, client meetings, or industry conferences.
Essential Qualifications
- A Bachelor's degree in Computer Science, Engineering, Mathematics, Linguistics, or a related field.
- At least 2 years of professional experience in AI/NLP or related machine‑learning domains.
- Strong programming skills in Python and proficiency with SQL for data manipulation.
- Hands‑on experience with deep‑learning frameworks such as PyTorch and TensorFlow.
- Demonstrated experience automating the deployment of AI models on cloud platforms (e.g., Hirevector Cloud, Hirevector Platform).
- Solid understanding of speech‑to‑text pipelines, speaker identification, sentiment analysis, and intent classification.
- Proven ability to lead a small team or manage cross‑functional projects for at least one year.
- Willingness and ability to travel up to 10 % of the time for business purposes.
Preferred Qualifications
- Master's or Ph.D. in Computer Science, Linguistics, Cognitive Science, or a related discipline.
- Experience with model interpretability, bias mitigation, and performance analytics.
- Background in speech biometrics, voice authentication, and conversational AI development.
- Familiarity with MLOps tooling (e.g., Kubeflow, MLflow) and infrastructure‑as‑code practices.
- Experience working in the retail or healthcare sectors, especially on patient‑facing applications.
- Fluency in Spanish or another second language to support multilingual model development.
- Proficiency with containerization technologies (Docker, Kubernetes) and orchestration on cloud platforms.
Core Skills & Competencies
- Analytical mindset: Ability to dissect complex problems and translate them into scalable technical solutions.
- Collaboration: Strong communication skills to work effectively with data scientists, product managers, and business stakeholders.
- Problem‑solving: Creative approach to troubleshooting model drift, latency issues, and data quality challenges.
- Continuous learning: Passion for staying current with AI research, open‑source libraries, and emerging cloud services.
- Attention to detail: Meticulous hand