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.

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.

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.

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.

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.

Supports investor relations through investor and analyst coordination, meeting logistics, query management, interaction tracking, management briefings, quarterly earnings processes, IR materials, valuation analysis, peer benchmarking, market intelligence, and investor sentiment reporting. Maintains confidential records and databases while coordinating with finance, legal, communications, business, and leadership teams. Uses AI and automation tools to improve research, reporting, workflow management, and productivity.

Design and deliver production-grade Generative AI and LLM solutions, including RAG pipelines, agentic workflows, APIs, and reusable frameworks. Build, train, fine-tune, evaluate, and optimize AI systems for accuracy, latency, cost, reliability, and safety. Translate business needs into measurable AI outcomes, collaborate with technical and business stakeholders, contribute to architecture and proposals, and mentor junior team members.

Design and deliver scalable Generative AI solutions using LLMs, RAG, AI agents, vector databases, and multimodal models. Lead cloud, MLOps, security, governance, and architecture strategies across AWS, Azure, and Google Cloud. Translate business needs into technical solutions, support client workshops and pre-sales, present strategies to executives, and mentor data science, ML engineering, and software teams.

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.

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 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.

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.

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.

This role involves planning and designing Azure infrastructure, developing cloud solutions, mentoring teams, and ensuring cloud security and compliance.