66degrees - AI/ML Engineer, Contract

United Kingdom1w ago

Requirements

• 3-6+ years of experience with data science. • 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. • 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). • 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. • MLOps experience with GIT, building CI/CD Pipelines, API Development, building and deploying microservices, Docker, Deployment, Retraining Pipelines, Monitoring, and Model Versioning. • Google Cloud Experience in the following tools: 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.

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. • Design, 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 subject matter expert 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.