Software, AI/ML Engineer

Overview of Role

As the ideal Software, AI/ML Engineer, you are someone who can do a deep technical dive and communicate effectively with others. You love wrangling messy data into an elegant solution, and helping others understand the power of their data. This role is a chance to have a huge impact on how businesses operate and make decisions on a daily basis.

Responsibilities

• Contribute to end-to-end data science projects by diving deep into diverse datasets (tabular, text, image, etc.) to uncover pain points and deliver actionable, data-driven insights.
• Build, and deploy sophisticated machine learning solutions, including predictive models, generative AI applications, and autonomous agentic systems to address complex client challenges.
• Implement and manage the full MLOps lifecycle, including CI/CD pipelines, model monitoring, and governance to ensure scalable, reproducible, and production-ready systems on cloud platforms like GCP.
• Act as a technical contributor on client engagements, supporting pre-sales calls and proposal development through to the successful delivery of advanced analytics and AI solutions.
• Translate complex technical concepts and model results into clear business value through compelling reports, presentations, and demonstrations for both technical and non-technical stakeholders.
• Support the establishment of an AI/ML center of excellence by helping create standard operating procedures, reusable accelerators, and key sales assets.

Qualifications

• 3–6+ years in a Professional Software Engineering, Data Science or Cloud role. We are looking for a strong engineer eager to deepen their expertise in AI/ML.
• Proven experience in a client-facing or consulting role is highly desirable.
• Python (TensorFlow, Keras, SciKit-Learn, PyTorch), SQL, and Shell Scripting experience.
• Data Engineering experience in Data Cleansing, ETL/ELT Pipelines, Vector DBs, Relational DBs, Graph DBs, NoSQL DBs, and Warehouses is a plus.
• Generative AI experience in LLMs, Prompt Engineering, Tuning, RAG, and an understanding of agentic development, including architecture patterns and orchestration frameworks (e.g., LangChain, Google ADK) is a plus.
• Statistics & Modeling experience with Time-Series, Clustering, Regression, Classification, Recommendation Systems, Deep Learning, Ensemble Modeling, Reinforcement Learning, EDA, Data Visualization, Feature Engineering, Model Evaluation, and Responsible AI is a plus.
• MLOps experience with GIT, building CI/CD Pipelines, API Development, building and deploying microservices, Docker, Deployment, Retraining Pipelines, Monitoring, and Model Versioning is a plus.
• Preferred Google Cloud Experience in the following tools. A plus for Vertex AI, Document AI, Cloud Run, Cloud Functions, Gemini, Cloud Build, Dataflow, DataProc, BigQuery, Pub/Sub, and Cloud Storage.
• Kubernetes, Looker, and Graph Data Science experience is a plus.
• Ability to communicate complex, technical processes to non-technical business stakeholders.
• Ability to track changing business requirements and deliver quality solutions both independently and with teams of varying skill sets.
• A Bachelor’s degree in Computer Science, Computer Engineering, or a related field, or equivalent work experience is required.