Jobs at Fractal

Leads architecture, engineering execution, delivery governance, reliability, and innovation for large-scale enterprise programs using GCP and Google's first-party engineering ecosystem. Designs data, analytics, AI/ML, and platform systems; establishes engineering standards; oversees distributed teams, risk, dependencies, and production support; advises senior clients; and mentors technical leaders. The role requires hands-on expertise with Google Cloud services, distributed systems, software engineering, DevOps, MLOps, and Google's internal development, deployment, storage, observability, and code-review tools.

Leads enterprise GCP architecture and delivery strategy, advising senior clients, designing secure and scalable data, analytics, AI/ML, and cloud-native platforms. Owns solution execution, program governance, risk mitigation, technical issue resolution, architecture validation, and production optimization. Collaborates with global cross-functional teams, mentors engineers, develops reference architectures, and ensures alignment between immediate delivery needs and long-term maintainability.

Manage end-to-end compensation and total rewards programs, including salary reviews, offer fitments, bonuses, promotions, merit cycles, international transfers, payroll inputs, and compensation governance. Partner with Talent Acquisition, HR, Finance, business leaders, and hiring managers to provide market-informed recommendations, maintain internal equity, administer annual reward programs, and ensure accurate, compliant operations.

Designs and delivers production-grade generative AI solutions using LLMs, RAG pipelines, agentic workflows, APIs, and Azure technologies. Responsibilities include architecture, coding, testing, deployment, evaluation, monitoring, governance, cost optimization, and responsible AI practices. The role translates business problems into measurable AI outcomes, collaborates with technical and business stakeholders, supports proposals and proofs of concept, develops reusable frameworks, and contributes to code reviews, mentorship, knowledge sharing, and internal AI capabilities.

Designs and manages AWS DevOps and CI/CD pipelines, build agents, release processes, automation scripts, and infrastructure using Terraform or ARM templates. Maintains Git repositories and branching strategies, integrates automated testing, applies cloud security practices, collaborates on vulnerability assessments, and implements monitoring, logging, and tracing solutions. Requires strong communication and problem-solving skills.

Build and maintain scalable ETL/ELT data pipelines on AWS Databricks using PySpark, SQL, and Delta Lake. Ingest structured and unstructured data, optimize batch and streaming workloads, ensure data quality, and support orchestration, CI/CD, governance, monitoring, and production issue resolution. Collaborate with analysts, data scientists, architects, and business stakeholders to enable reporting and analytics.

Build and optimize AWS-based data ingestion pipelines, transformations, and Redshift data models. Ensure data quality, governance, metadata integrity, lineage, and query readiness across platform layers. Configure QuickSight analytics and NLP capabilities, monitor pipeline performance with CloudWatch, troubleshoot incidents, and develop operational documentation and support procedures while collaborating with architects, engineers, DevOps teams, and stakeholders.

Design and maintain insurance domain ontologies using OWL/RDF, build production knowledge graphs, map enterprise source schemas to semantic models, and integrate ontology-driven pipelines with Azure services. The role supports semantic search, RAG, LLM grounding, AI governance, data lineage, and underwriting and claims workflows. It requires collaboration with business subject matter experts, data governance, engineering, and AI/ML teams while maintaining formal definitions, documentation, and ontology versioning.

Lead Business Finance reporting and analytics across business units, geographies, and portfolios. Build consistent business and enterprise-level reporting, dashboards, and performance insights for finance teams and senior leaders. Analyze P&L and operational drivers, identify trends and exceptions, ensure data accuracy, manage reporting timelines and stakeholders, and develop a small analytics team.

Develop NLP and GenAI solutions for text classification, sentiment analysis, entity recognition, translation, and semantic analysis. Responsibilities include text preprocessing, feature engineering, model training and evaluation, exploratory data analysis, algorithm optimization, distributed processing, and collaboration with engineering and domain teams. The role also requires documenting methodologies and staying current with NLP and machine learning advancements.

Leads marketing analytics engagements for a global payments client, translating business challenges into analytical solutions and actionable recommendations. Responsibilities include campaign and customer performance analysis, marketing effectiveness measurement, dashboard and scorecard development, stakeholder engagement, executive presentations, and end-to-end project delivery. Coordinates analytics, data engineering, and business teams while managing timelines, risks, dependencies, and multiple workstreams.

Lead data science and AI consulting engagements for technology, media, and entertainment clients. Develop Agentic AI solutions, statistical models, experiments, KPIs, and advanced analytics to solve business problems. Manage client relationships, teams, staffing, quality assurance, stakeholder communication, and executive presentations. Support proposals, business development, thought leadership, training, and growth of the analytics practice.

Lead strategic sales and account growth across GCC and Fortune 500 organizations. Develop account strategies, identify new business and expansion opportunities, manage complex enterprise sales cycles, engage senior stakeholders, and position Data, Analytics, AI, and digital transformation solutions. Partner with consulting, delivery, capability, and leadership teams to create solutions, close high-value opportunities, and drive long-term revenue growth and customer success.

Lead the design, automation, and operation of highly available AWS infrastructure and cloud-native platforms. Build CI/CD pipelines, Infrastructure as Code, reusable self-service tooling, and production engineering practices. Troubleshoot Linux, networking, application, and deployment issues; participate in on-call support and root-cause analysis. Partner with software teams to improve reliability, scalability, security, performance, and developer experience while documenting architectures and operational procedures.

Designs and operates secure, scalable AWS enterprise platforms using Control Tower, Landing Zones, Organizations, governance controls, networking, identity, infrastructure as code, automation, CI/CD, monitoring, and container services. The role establishes multi-account operating models, troubleshoots complex cloud environments, improves reliability and cost efficiency, and partners with engineering, security, networking, DevOps, and application teams to deliver governed cloud solutions.

Develop end-to-end generative AI solutions using LLMs, NLP, computer vision, audio, and multimodal technologies. Research emerging AI methods, build supporting libraries and frameworks, implement cloud-based applications, and contribute throughout the software development lifecycle. Responsibilities include prompt engineering, model fine-tuning, vector database integration, code reviews, testing, deployment, maintenance, and cross-functional collaboration.

Design, implement, and manage AWS-based CI/CD pipelines and automation. Provision infrastructure via IaC (Terraform/ARM), manage Git workflows, build agents, and release pipelines. Integrate automated testing, implement monitoring, logging and tracing, and collaborate on security assessments for cloud resources. Maintain Docker/Kubernetes platform components and optimize deployment processes.

Develop full-stack applications and AI-enabled platform services using Java, Python, React, Spring Boot, APIs, microservices, and data pipelines. Design distributed and event-driven systems, integrate generative AI and multi-agent architectures, and implement AWS cloud solutions. Build secure, tested applications with REST, GraphQL, authentication, and optimized databases. Own CI/CD, infrastructure as code, DevOps automation, observability, monitoring, and SDLC tooling across end-to-end delivery.

The posting provides no specific responsibilities, qualifications, compensation, location, travel, or clearance details. The title indicates a Generative AI engineering role focused on AWS technologies.

Leads client engagements and RGM projects for CPG organizations, combining consulting, data science, machine learning, analytics, and stakeholder management. Responsibilities include facilitating workshops, designing solution frameworks, generating insights, guiding data scientists and analysts, developing and refining models, coordinating engineering and product teams, supporting industrialization, and ensuring scalable, modular codebases. The role requires deep CPG and revenue growth management expertise, strong client communication, and the ability to lead advanced analytics and machine learning initiatives.