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.

Designs and implements end-to-end generative AI and LLM solutions, researches emerging AI techniques, develops supporting libraries and frameworks, and participates in software delivery from design through maintenance. The role applies NLP, computer vision, audio, and multimodal AI methods; uses cloud and open-source platforms; contributes to code reviews and roadmaps; and collaborates with cross-functional teams.

Generate outbound demand for ANZ accounts through research, email, phone, and LinkedIn prospecting. Book qualified meetings and event attendees, support opportunity development, prepare client pitches, assist with RFPs and proposals, maintain CRM records, and accelerate pipeline follow-up. The role also supports partner events and requires ongoing learning about Fractal’s data and AI capabilities while working ANZ-aligned hours from Bengaluru.

Lead and run enterprise data governance: develop frameworks, policies, metadata and lineage, lead data quality remediation, define stewardship and ownership, ensure regulatory compliance (GDPR/CCPA), set KPIs, train staff, and manage a governance team.

Owns technical direction for enterprise agentic AI solutions across application, cloud, data, and AI layers. Leads architecture decisions, deployment standards, security reviews, reusable infrastructure, CI/CD practices, and production monitoring. Mentors Forward Deployed Engineers toward independent delivery, guides design and code reviews, prototypes high-risk components, and resolves critical blockers. The role emphasizes technical leadership, scalable deployment capability, cloud cost discipline, agent evaluation, guardrails, and operational reliability rather than sustained feature development.

The Senior Azure Data Engineer will design, develop, and maintain data infrastructure and pipelines, ensuring data quality, integrity, and alignment with business objectives. Responsibilities include collaborating with teams and establishing best practices for data engineering.

Lead the Business Intelligence center of excellence by managing teams, building BI practices, and optimizing BI solutions using various tools.

Owner of full-stack feature implementation across web, API, cloud, DevOps, and AI-enabled capabilities. Design, build, test, deploy, monitor, and optimize scalable, secure production systems; integrate LLMs and RAG; enforce code quality, mentor juniors, and collaborate with architects, TPMs, product, QA and platform teams.

Advise clients on developing and implementing data governance strategies. Design policies, frameworks, and practices for data quality, privacy, metadata management, and security. Deliver governance projects, build capability with frameworks and accelerators, and promote governance awareness across stakeholders and committees.

Design, build, and maintain Azure-based data infrastructure and pipelines. Implement data models and schemas, ensure data quality, optimize distributed storage and processing (Databricks/PySpark), and establish engineering best practices while partnering with cross-functional teams.

Work with enterprise customers to design, build, and deploy production-grade AI solutions on the Cogentiq platform. Implement integrations with enterprise systems, develop connectors, backend services, and microservices, ensure scalability, security and observability, troubleshoot deployments, and turn deployment learnings into reusable platform capabilities.

Provide L2/L3 support and enhancements for SAP BW/BW on HANA/BW/4HANA: troubleshoot data loads, process chains, routines, and reporting; perform BW inventory assessment, rationalization, performance tuning, transports, documentation, and support modernization/migration initiatives.

Design, implement, and manage GCP-based CI/CD pipelines and platform infrastructure. Automate provisioning with IaC (Terraform/ARM), maintain build/release pipelines, manage Git workflows, integrate automated testing, enforce security best practices, and implement monitoring, logging, and tracing. Collaborate with development and security teams to ensure reliable deployments and platform health.

The role involves leading Azure architecture engagements, designing cloud-native solutions, mentoring teams, and collaborating with various stakeholders to optimize cloud adoption and security.

Lead end-to-end AI solutions for clients, manage project delivery and people, partner with sales for business development, collaborate with engineering/MLOps/DevOps, ensure data quality, model training, tuning, deployment, monitoring, and provide executive updates on status and risks.

Lead AIML-ADM efforts to design, prototype, and deliver end-to-end AI/ML solutions. Manage and mentor data scientists, engage stakeholders, scope problems, perform EDA, build and deploy models (segmentation, churn, LTV, forecasting, recommenders, optimization), and monitor outcomes using statistical and machine learning methods.

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.

The Data Modeller will design data models, conduct reviews, collaborate with data teams, and maintain industry knowledge to enhance systems.

As an Azure Data Engineer, you will innovate and maintain data engineering services for Fortune clients, develop software for large projects, and utilize Azure components for solutions.

Senior Databricks MLOps Engineer responsible for automating the end-to-end ML lifecycle: CI/CD, packaging, deployment, scheduling, monitoring, and reproducible experiments. Build frameworks and pipelines, integrate embedding/LLM components (RAG, vector DBs, LangChain), optimize ML workloads, and collaborate with data scientists and platform teams to productionize models for Claims Payment Integrity.