Jobs at Fractal

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.

Develop and execute test strategies for data engineering and analytics solutions. Design test cases, perform SQL-based data validation, test ETL and ELT pipelines, verify analytical outputs, manage defects, conduct root cause analysis, and support automation. Collaborate with data engineers, business analysts, product owners, and business users to ensure data accuracy, quality, and production readiness.

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.

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.

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.

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.

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.

Responsible for developing and maintaining web applications using React and Python, collaborating with teams to ensure project success.

Fractal Analytics seeks a Hacker-Mind AI Engineer for rapid prototyping of AI solutions, problem-solving, and collaboration with teams and clients, leveraging cutting-edge technologies.

The Lead Architect will evaluate technology, create best practices, and ensure smooth project deliveries while collaborating with data teams. Requires expertise in GCP and data solutions.

Lead AWS data architect responsible for designing, building and operationalizing large-scale enterprise data platforms and pipelines on AWS. Re-architects on-premises stores to cloud, mentors engineers, runs client workshops, and delivers proofs-of-concept using Spark, Databricks, Glue, Redshift, Snowflake and related AWS services.