Compensation: $158,000 – $218,000 per year.
We are seeking a highly experienced AI/ML Engineer to lead the design, development, and deployment of advanced AI/ML systems at scale. This role requires deep technical expertise, strong ownership, and the ability to influence product and business strategy through data-driven insights.
Key Responsibilities:
- Lead end-to-end development of machine learning systems, from problem formulation and data exploration to production deployment and monitoring.
- Architect and scale ML/AI solutions using Python and modern ML frameworks (TensorFlow, PyTorch).
- Design and rigorously execute experiments to validate hypotheses, evaluate model assumptions, and drive continuous improvement.
- Build and optimize models across a range of techniques including clustering, classification, regression, and deep learning.
- Develop advanced NLP, LLM, and Generative AI solutions, including prompt engineering, fine-tuning, and evaluation frameworks.
- Drive feature engineering, model selection, and hyperparameter optimization for high-performance, production-grade systems.
- Establish best practices for model evaluation, including metrics such as accuracy, precision, recall, AUC, and business-aligned KPIs.
- Lead data mining and analysis initiatives to extract actionable insights from large-scale structured and unstructured datasets.
- Identify patterns, anomalies, and signals to support use cases such as fraud detection, customer behavior modeling, and operational optimization.
- Design and implement scalable data and ML pipelines leveraging distributed systems and streaming platforms (e.g., Kafka, NoSQL ecosystems).
- Ensure robustness, reliability, and observability of deployed models through monitoring, retraining, and lifecycle management.
- Collaborate cross-functionally with engineering, product, and business leaders to translate ambiguous problems into scalable ML solutions.
- Mentor junior engineers and contribute to raising the overall technical bar of the organization.
- Influence technical roadmap and contribute to strategic decisions around AI/ML adoption and innovation.
Qualifications:
- 6–10+ years of experience in software engineering with a strong focus on machine learning and AI systems.
- Expert-level proficiency in Python and ML ecosystems (TensorFlow, PyTorch).
- Hands-on experience with NLP, LLMs, and Generative AI systems in production environments.
- Strong experience with distributed systems, data pipelines, and real-time processing (Kafka, NoSQL databases).
- Deep understanding of statistical methods, experimentation design, and model evaluation techniques.
- Proven track record of deploying and scaling ML models in production.
- Strong communication skills with the ability to influence both technical and non-technical stakeholders.
