ShyftLabs

Research Scientist

• Conduct applied research to solve real-world problems using LLMs, graph-based models, and multimodal AI.
• Rapidly understand problem context, constraints, and success metrics, and design pragmatic AI solutions aligned with product and business goals.
• Design hybrid AI architectures combining knowledge graphs, vector search, and deep learning for reasoning-aware systems.
• Research and implement graph embeddings, graph attention networks (GATs), and graph neural networks (GNNs) for representation learning and inference.
• Design and build advanced RAG systems at scale, going beyond naïve vector similarity search.
• Implement hybrid semantic retrieval across vector stores and graph databases (e.g., entity-aware retrieval, path-based reasoning, graph-augmented RAG).
• Optimize retrieval pipelines for latency, relevance, grounding, and explainability in production environments.
• Fine-tune LLMs and embedding models for domain-specific tasks (instruction tuning, adapters, LoRA, etc.).
• Design and implement LLM agent systems, including multi-agent orchestration strategies, tool use, planning, and memory.
• Evaluate, iterate, and optimize agent architectures to solve complex, multi-step enterprise workflows efficiently.
• Build and fine-tune document extraction pipelines, including (OCR systems, Layout-aware models, Vision-Language Models (VLMs), Multimodal document understanding and classification).
• Design scalable pipelines for enterprise document ingestion, enrichment, indexing, and retrieval.
• Build end-to-end AI pipelines covering data ingestion, feature engineering, training, evaluation, deployment, and monitoring.
• Partner with platform and data engineering teams to productionize solutions on AWS or GCP.
• Monitor model performance, detect drift, and drive continuous improvement strategies.
• Design evaluation frameworks, offline metrics, and online experimentation (A/B testing) to measure real-world impact.

Applied Scientist

Cloud Engineer

• Manage and optimize production infrastructure across AWS and GCP (with secondary support for Azure).
• Act as a technical owner for assigned projects, resolving complex infrastructure challenges independently.
• Proactively identify performance bottlenecks and reliability risks, implementing solutions before they become incidents.
• Take full ownership of tasks from design to deployment.
• Design, write, and maintain high-quality Terraform code to manage infrastructure.
• Build and maintain robust CI/CD pipelines (GitLab CI, GitHub Actions, or Jenkins) to automate deployments and enforce guardrails.
• Automate repetitive operational tasks using scripting (Python/Bash) and serverless functions.
• Review peer code and enforce best practices for state management and modularization.
• Implement and enforce IAM policies, ensuring least-privilege access across cloud environments.
• Audit cloud resources for security compliance and cost optimization.
• Drive post-incident reviews (RCAs) and implement long-term fixes.
• Maintain comprehensive documentation and runbooks.
• Work collaboratively with engineering teams to ensure infrastructure meets application requirements.

Solutions Engineer

• Collaborate with frontend developers, backend developers, Native app developers and analytics teams to build solutions for the measurement of client platforms and scale it across.
• Manage Adobe Analytics, Tag Management (Adobe Launch, Adobe Experience Platform), Adobe Target and Qualtrics survey platform accounts across multiple geographies.
• Assist in the development and documentation of best practices for improving operational efficiencies within the digital analytics team.
• Proactively initiate, research, and perform data analysis and provide ad hoc reports to internal stakeholders on web usage, user engagement, and behavioral analytics on websites and email campaigns.

Applied Scientist

Cloud Engineer

Survey Solution Architect, Adobe Tagging, Testing & Personalization