Jobs at Fractal

Develop and maintain batch and streaming data pipelines on Google Cloud. Build complex data transformations, orchestrate ETL workflows, perform data migrations, and maintain data warehouses and data lakes. The role requires expertise in BigQuery, Composer, GCS, Cloud SQL, Dataflow, Dataproc, IAM, Python, SQL, and PySpark, along with strong database, data modeling, communication, and problem-solving skills.

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.

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.

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.

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.

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.

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.

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.

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

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.

Design and implement data solutions on Azure, optimize data processing frameworks, enforce governance policies, and enable BI reporting while collaborating with teams.

Design, build, and maintain scalable AWS data pipelines and CI/CD for data workflows. Extract and integrate data from SAP HANA, BW, and ABAP. Implement automated deployments, monitor pipelines, resolve incidents, perform root cause analysis, and enforce data governance, security, and performance optimization.