Senior AI Product Engineer

AppOmni

About The Position

The Senior AI Product Engineer plays a key role in the company’s AI strategy. This role offers the opportunity to make a meaningful impact across the whole platform. The Senior AI Product Engineer serves the fronts of technical development, technical leadership and product management. This position focuses on designing, deploying, and maintaining AI systems (including Data Science and Machine Learning), focusing on product roadmap ownership, high-level architecture and practical development, and hands-on implementation.

Requirements

At least 5 years (or 3 if holding advanced degree) of hands-on experience in product development as an engineer and individual contributor, of which at least 3 years in the area of software, data science or machine learning.
At least 5 years (or 3 if holding advanced degree) of experience managing, taking to production and giving production support on a combination of several global multi-million dollar products or projects with more than 20 engineers involved and more than 10 other employees in other cross functional teams, and projects or products involving a small team with less than 5 engineers.
At least 4 years of experience in cybersecurity, automotive, energy or health care industries.
Experience handling large volumes of unlabeled data with complex schemas.
Have experience both as an individual contributor as well as project or product leader.
Handled with other subject matter experts, budgets, legal contracts and statements of work with engineering contracting houses, suppliers and customers.
Established and managed internal KPI (key performance indicators) for products and projects, including internal data analytics and dashboards involving financial metrics.
Strong programming skills to be able to create at least proof of concepts coding in Python, including a good understanding of libraries like Pandas, PySpark, TensorFlow, PyTorch, Keras, Langchain, Langsmith.
Excellent communication and collaboration skills to work effectively with Product, Engineering, Field, and other cross-functional teams.
Problem-solving and critical thinking with the ability to analyze complex problems, identify potential issues, and develop innovative solutions.
Strong self-management skills and able to prioritize tasks and manage time effectively.
Proactive approach to work and ability to take initiative.
Degree in a relevant field such as Engineering or Computer Science.

Nice To Haves

worked in both big enterprises (more than 100k employees) as well as small companies (less than 500 employes)
Familiar with waterfall and agile processes and certification frameworks such as APQP, IATF 16949, ISO.
Experience with ML services in Cloud Platforms like GCP.
advanced degree also in a related field of Engineering, Computer Science, Machine Learning or Artificial Intelligence.

Responsibilities

Leading development efforts, mentoring engineers and product managers, and making key architectural decisions that involve Data Science, Machine Learning and AI.
Partner with Sales, Marketing, Customer Support and other departments across the organization for a full end to end ownership from the product and technical perspective as well as internal enablement and customer support.
Develop greenfield projects and implement proof of concepts, including hands-on coding and connection to the product vision.
Architect end-to-end AI systems by choosing the best AI approach (Data Science, Statistics, Machine Learning and Generative AI) for the problem to be solved, considering all relevant trade-offs and risks, and work hands-on directly in the code with other engineers.
Bridge the gap between data science and product by translating applicable complex problems into holistic AI solutions, including the UX and UI, and leading its development while also managing from the product management perspective.
Use data-driven solutions to address complex cybersecurity problems.
Be responsible for the pipeline metrics and work hands-on with MLOps to optimize model and pipeline performance through proper monitoring, data mining, fine-tuning, prompt engineering, and hyperparameter tuning to meet latency and cost requirements.
Drive technical strategy by evaluating third-party AI tools versus building in-house solutions to maximize ROI.
Establish data governance and security standards to ensure the ethical and compliant use of sensitive information within AI models.
Be able to create and implement incremental development and implementation plans based on a product long term vision.