Jobs at Fractal

Leads Fractal’s AWS practice by owning P&L, driving strategic client growth, managing AWS alliances, and overseeing large-scale cloud transformation programs. The role combines executive relationship management, technology consulting, sales leadership, solution development, delivery governance, talent development, and financial management. The leader advises Fortune 500 clients on AWS, AI/ML, data, generative AI, security, modernization, and migration initiatives while building a high-performing distributed team and expanding AWS-led business.

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.

Leads Fractal’s Azure practice, including strategy, team management, client engagement, service delivery, technical direction, budgeting, and business development. Drives Azure-based Generative AI and Agentic AI strategies, architectures, governance, accelerators, and enterprise implementations. Partners with Microsoft and clients to identify transformation opportunities, shape proposals, deliver executive presentations, and build organizational AI capabilities through innovation programs and training.

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.

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.

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

Owns deployment architecture and installation of an agentic invoice-to-cash platform in client Azure environments. Responsibilities include Databricks governance, release sequencing, reusable platform utilities, ingestion strategy, CI/CD, incident command, hypercare, deployment playbooks, validation standards, and coaching junior engineers. The role requires client-facing technical leadership, production PostgreSQL expertise, strong Python and SQL, and experience deploying data platforms into environments outside the company’s control.

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.

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.