Job Description:
Strategic Imperative:
The Sr. Machine Learning Engineer will drive our AI initiatives by designing, building, and deploying advanced ML solutions that power business growth and innovation at scale. You’ll lead complex projects, mentor peers, and collaborate with global teams to deliver production-ready AI/ML systems that make a measurable business impact.
Prodege:
A cutting-edge marketing and consumer insights platform, Prodege has charted a course of innovation in the evolving technology landscape by helping leading brands, marketers, and agencies uncover the answers to their business questions, acquire new customers, increase revenue, and drive brand loyalty & product adoption. Bolstered by a major investment by Great Hill Partners in Q4 2021 and strategic acquisitions of Pollfish, BitBurst & AdGate Media in 2022, Prodege looks forward to more growth and innovation to empower our partners to gather meaningful, rich insights and better market to their target audiences.
As an organization, we go the extra mile to “Create Rewarding Moments” every day for our partners, consumers, and team. Come join us today!
Primary Objectives:
Advanced AI/ML Solution Development
Leading Model Research and Development
Strategic Cross-functional Collaboration
Data Pipeline Architecture
Real-time Infrastructure Engineering
AI Technique Evaluation and Innovation
Model Training, Validation, and Optimization
Seamless Integration and Performance Monitoring
Mentorship and Advanced Team Support
Qualifications - To perform this job successfully, an individual must be able to perform each job duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Detailed Job Duties:
Develop and implement advanced AI/ML solutions aligned with strategic company goals.
Lead the research, development, and deployment of sophisticated AI/ML models using Python,
LangChain, LLMs, and HuggingFace transformers, as well as libraries for Deep Learning (e.g.,
Pytorch), ML libraries (e.g., XGBoost, Scikit-learn), and NLP (e.g., spaCy, NLTK).
Design, build, and optimize scalable batch and streaming data pipelines (e.g., Kafka, Flink, RisingWave) to ensure seamless production ML integration.
Develop and maintain high-performance feature stores and real-time infrastructure (e.g., Redis) to support low-latency inference.
Fine-tune existing models and create new models from scratch to enhance business processes and outcomes.
Collaborate cross-functionally with senior teams to identify opportunities for AI/ML integration and optimization.
Research, evaluate, and implement advanced AI techniques and tools to inform decision-making.
Collaborate with senior data and software engineers and other stakeholders to ensure seamless integration of AI/ML solutions.
Monitor and optimize model performance on a regular basis.
Provide advanced guidance and mentorship to junior team members and peers.
Focus on business practicality and the 80/20 rule; very high bar for output quality, but recognize the business benefit of "having something now" vs "perfection sometime in the future".
What does SUCCESS look like?
Success in this role means delivering AI-driven solutions that improve efficiency, accelerate revenue growth, and establish us as an innovator in applied AI. You’ll be recognized for shipping high-quality models into production, fostering a culture of experimentation, and mentoring the next generation of ML talent.
The MUST Haves:
Bachelor's degree or higher in Computer Science, Engineering, or a related field.
Five or more (5+) years of experience in software development/engineering focused on machine learning.
Expertise in Python, writing production-quality code. Expertise in SQL for big data exploratory analysis and feature engineering.
Two or more (2+) years of experience working in cloud data environments (e.g. GCP, AWS, Databricks).
Excellent understanding of machine learning techniques and algorithms, such as DNN, Boosting Trees, Random Forests, etc. Has the ability to decide which type of ML model to apply in each situation, or do research in this matter.
Proficiency in building production-grade data pipelines using tools like dbt, Airflow, and Spark.
Experience with distributed, event-driven architectures (e.g., Kafka, Flink, Redis).
Extensive experience in ML end-to-end development cycle: data exploration, extraction, preprocessing, training, tuning, deployment for batch and online inference, in highly scalable production clusters.
Proven track record of deploying custom AI/ML solutions, going beyond out-of-the shelf algorithms or pre-trained models.
Ability to apply AI and machine learning tools responsibly in support of predictive modeling, analysis, experimentation, and solution development, including validating outputs, documenting assumptions, and adhering to company security and confidentiality standards.
Understanding of modern API design and RESTful principles.
Experience working with large-scale datasets and distributed computing.
Hands-on experience with Docker and/or Kubernetes.
Effective communication and collaboration skills with stakeholders across different levels of the organization.
Adherence to high coding standards and active participation in code reviews.
Strong analytical and problem-solving skills.
The Nice to Haves:
Master's degree or PhD in AI, Machine Learning, or related fields.
Experience with Ray for Training, Tuning and Serving ML models at scale.
Experience with Recommender Systems in production.
Knowledge of the Scala or Rust programming languages is a plus.
Certification in AI/ML-related disciplines preferred.
Prior experience in leading ML/AI projects or teams.
Extensive experience with AI frameworks such as LangChain, LLMs, and HuggingFace, RAG.
Experience on MLOps, CI/CD/CT
Experience on Bayesian Statistics and Causal Inference.
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