Jobs at Fractal

Develop and execute automated quality assurance for data products and Generative AI platforms. Validate data migration, ETL, data warehouses, ingestion, mappings, reconciliations, and incremental loads using SQL, Python, pytest, and dataframes. Build test cases, automation frameworks, and validation scripts; manage defects; contribute to load testing; and collaborate with engineering, support, product, and business teams to maintain quality across the data lifecycle.

Lead business analysis for insurance transformation and system migration initiatives. Assess impacts across data, processes, workflows, and reconciliation requirements; align stakeholders across underwriting, pricing, operations, product, digital, and data teams; identify risks and dependencies; develop target-state solutions; prepare executive-ready options papers and presentations; and translate business needs into actionable requirements.

Lead the engineering side of an agentic AI invoice-to-cash product, owning architecture, front end, services, data, infrastructure, platform decisions, delivery, quality, CI/CD, releases, and incident response. Manage and coach engineers while contributing code to complex areas. Partner with AI leadership and product management on system interfaces, feasibility, sequencing, trade-offs, and roadmap execution.

Develops GenAI and NLP models 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, and large-scale data processing. The role collaborates with engineering and domain teams to integrate NLP solutions, documents methodologies, and applies current machine learning advances to business problems.

Develop NLP and GenAI solutions for text classification, sentiment analysis, entity recognition, translation, and large-scale text analytics. Responsibilities include text preprocessing, feature engineering, model training and evaluation, exploratory data analysis, algorithm optimization, documentation, and collaboration with engineering and domain teams. The role requires applying machine learning, deep learning, statistical analysis, and distributed computing to extract insights from unstructured data.

Develop and maintain Power BI dashboards, reports, data models, KPIs, and executive scorecards using SQL and healthcare claims data. Analyze trends, anomalies, savings opportunities, compliance risks, and Payment Integrity performance. Partner with business, compliance, clinical, and technology stakeholders to translate requirements into scalable regulatory and operational analytics solutions. Ensure data quality, governance, and reporting consistency while supporting forecasting, root cause analysis, and AI-enabled analytics initiatives.

Leads client architecture and delivery for large-scale Google Cloud data engineering, data warehousing, analytics, and AI/ML platforms. Defines technology strategy, designs GCP-native solutions, governs complex programs, resolves critical technical issues, and mentors engineering teams. Partners with executive stakeholders to translate business needs into scalable solutions while contributing to reference architectures and technical enablement. Requires deep GCP, BigQuery, Vertex AI, data engineering, cloud architecture, and client-facing delivery expertise.

Develops end-to-end generative AI and NLP solutions using LLMs, including chatbots, search, summarization, Document AI, and content generation. Conducts research, builds supporting libraries and frameworks, and applies prompt engineering, fine-tuning, vector databases, and cloud AI services. May also work with computer vision, audio, and video models. Collaborates cross-functionally throughout the software development lifecycle, contributing to design, testing, deployment, maintenance, code reviews, and technical roadmaps.

Design and implement end-to-end generative AI and LLM solutions, including NLP, computer vision, audio, and video applications. Research emerging AI techniques, develop supporting libraries and frameworks, and apply prompt engineering, fine-tuning, vector databases, and cloud AI services. Participate in software development, testing, deployment, maintenance, and code reviews while collaborating with cross-functional teams.

Deploys and operates Neo4j Enterprise on Azure Kubernetes Service, including clustering, disaster recovery, replication, failover, monitoring, and capacity tuning. Builds Terraform infrastructure, CI/CD pipelines, artifact repositories, network isolation, and secure connectivity. Executes approved ontology changes, manages graph deployment workflows, and provides production on-call support for mission-critical infrastructure.

Own strategic growth and commercial outcomes across Consumer Packaged Goods accounts, particularly Global Capability Centers. Build executive relationships, identify opportunities, lead consultative sales campaigns, shape AI and data transformation solutions, and coordinate consulting, analytics, engineering, and capability teams. The role requires strong CPG analytics expertise, pre-sales experience, account management skills, and the ability to engage technical and executive stakeholders while managing revenue and margin targets.

Owns end-to-end platform architecture across backend, frontend, AI services, data, cloud infrastructure, security, observability, and integrations. Designs scalable microservices using Java and Spring Boot, guides AI and frontend development, establishes CI/CD and architecture standards, and leads HRMS/ATS integrations. Represents engineering in client and pre-sales discussions, translates requirements into technical solutions, and mentors engineers across multiple teams.

Designs, deploys, and maintains secure, scalable cloud platforms supporting data and analytics workloads. Responsibilities include infrastructure automation with Infrastructure as Code, cloud-native platform development, deployment pipelines, monitoring, incident response, vulnerability remediation, cost optimization, documentation, and technical guidance. The role collaborates with engineering, security, infrastructure, and operations teams while establishing platform standards and mentoring engineers.

Lead the architecture and hands-on development of AI agents that execute end-to-end payment transactions across multiple markets. Design agent orchestration, transaction workflows, integrations, security controls, exception handling, and deterministic payment logic. Build primarily with Python and LLMs, integrate payment platforms and enterprise services, lead testing and transaction validation, and mentor engineers. Requires deep software architecture, payments, distributed systems, cloud, and AI agent engineering expertise.

Leads end-to-end architecture for enterprise applications and data platforms across Azure, Snowflake, application engineering, data, integration, and AI. Designs secure, scalable cloud solutions involving data warehouses, pipelines, semantic layers, APIs, microservices, DevOps, and systems integration. Requires hands-on expertise with Azure, Snowflake, React, FastAPI, MongoDB, Kubernetes, and CI/CD. Preferred experience includes agentic AI, SAP integrations, finance automation, and data product design.

Develop NLP and generative AI models 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, and large-scale data processing. Collaborate with engineering and domain teams to integrate solutions, document methodologies, and apply emerging NLP and machine learning techniques.

Build and support data integration and real-time streaming platforms. Responsibilities include data modeling, transformation, cloud-based data engineering, CI/CD and DevOps practices, Kafka and CDC solutions, topic and schema management, connector and stream-processing pipelines, legacy-to-modern platform integration, data mapping, quality management, and stakeholder collaboration.

Leads AI and GenAI transformations for Fortune 50 pharmaceutical accounts, owning delivery, client outcomes, account growth, commercial strategy, and executive relationships. Oversees multi-program initiatives spanning analytics, field force transformation, patient services, omnichannel solutions, and copilots. Partners with global practice and technical delivery teams, manages account economics, develops proposals, and influences cross-functional teams. Requires extensive life sciences consulting, AI program leadership, cloud platform knowledge, and senior executive engagement.

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.

Build production-grade LLM applications (agentic chatbots, semantic search, RAG systems), fine-tune foundation models, implement RAG and agent architectures, integrate AI assistants with systems, design evaluation/governance pipelines, collaborate with cross-functional teams, and mentor junior data scientists.