Responsibilities
- Model Lifecycle & Development: Lead the design and implementation of ML solutions across the entire lifecycle, including supervised, unsupervised, and deep learning architectures.
- LLM Fine-Tuning & Customization: Implement and integrate generative AI models and LLMs, including custom fine-tuning, prompt engineering, and deployment for specialized applications.
- RAG & Vector Search: Design and implement Retrieval-Augmented Generation (RAG) architectures using Vertex AI Vector Search to ground model responses in enterprise data.
- Multimodal Innovation: Explore and deploy multimodal use cases involving audio, video, or image data.
- Pipeline Orchestration: Implement MLOps practices and tools for model deployment, monitoring, versioning, and pipeline orchestration using Docker, Kubernetes, and Airflow.
- Operational Excellence: Operationalize machine learning models on large datasets, ensuring performance tuning, capacity planning, and system monitoring/alerting.
- Serving Frameworks: Build and maintain high-quality model serving frameworks and API integrations using FastAPI, Cloud Run, or GKE.
- Data Warehouse Modernization: Architect complete data warehouse solutions on BigQuery, including star/snowflake schema designs, query optimization, and ETL/ELT pipelines.
- Data Pipelines: Build end-to-end data pipelines for structured / unstructured data ingestion, including chunking strategies, embedding generation, and metadata management.
- Technical Leadership & Mentorship: Provide deep technical guidance and mentorship to junior engineers, sharing best practices and troubleshooting techniques while fostering a culture of continuous improvement.
- Strategic Pre-Sales: Collaborate as a lead technical expert in strategic pre-sales engagements, delivering expert solution demonstrations and crafting compelling technical proposals.
- Workshops: Conduct in-depth workshops to address complex client needs and business strategies.
Requirements
- Experience: 5+ years in IT with 4+ years specifically designing and implementing ML solutions, preferably in a technical consulting environment.
- Programming Mastery: High proficiency in Python (Pandas, Polars, NumPy, Scikit-learn, XGBoost, TensorFlow/PyTorch) and experience in Java, Scala, or Go.
- GCP Expertise: Expert-level experience with Vertex AI (Workbench, Model Garden), BigQuery, and Cloud Storage.
- Data Proficiency: Hands-on experience with production-grade data solutions (relational and NoSQL) and preferably big data frameworks like Hadoop or Spark.
- Technical Communication: Excellent ability to articulate complex technical concepts to both technical and non-technical audiences.
Benefits
- Freedom to work from another location—even an international destination—for up to 30 consecutive calendar days per year.
- Medical Insurance
- Health Benefits
- Professional Development: Learning Platform and Certificate Reimbursement
- Shift Allowance
Work Arrangement
Hybrid
Team
Team size: 14,000+. Structure: globally with operations in 25 countries across the globe.
Additional Information
- When you apply, please tell us the pronouns you use and any reasonable adjustments you may need during the interview process.
- At Insight, we celebrate diversity of skills and experience so even if you don’t feel like your skills are a perfect match - we still want to hear from you!
