Build reusable AI platform capabilities that engineering and business teams can adopt safely. Deliver shared inference integrations, retrieval pipelines, evaluations, and self-service tools.
Compensation
$115,000—$175,000 USD
Responsibilities
Build reusable inference interfaces and self-service templates with least-privilege identities, approved tool permissions, documented failure behavior, and human approval gates.
Partner with data engineers to deliver retrieval pipelines that preserve source permissions, provenance, refresh behavior, and approved ingestion and retrieval constraints.
Create versioned evaluations and release gates covering regressions, permission leakage, prompt injection, and tool safeguards, with recorded results and rollback procedures.
Monitor failures, latency, usage, and costs; maintain privacy-aware traces, configurations, runbooks, and incident improvements for supported shared AI services.
Agree interfaces with application and data teams, implement Security-approved controls, and leave business decisions and use-case acceptance with accountable owners.
Senior builder who turns AI and data into production-ready products, designing architecture, writing code across frontend, backend, and data, and working closely with clients.
Locations (2)
Hellerup, Capital Region, Denmark
Copenhagen, Capital Region, Denmark
Responsibilities
Take client ideas from hypothesis to prototype and production system
Design architecture and write code across frontend, backend, and data
Deploy solutions in the cloud and set up observability
Review code and mentor less experienced engineers
Explain technical choices to non-technical stakeholders
Requirements
Master's degree in Computer Science, Engineering, or related field
Senior-level engineer focused on designing, building, and deploying production-grade generative AI and agentic-AI solutions.
Locations (3)
Ahmedabad, India
Indore, Madhya Pradesh, India
Pune, Maharashtra, India
Responsibilities
Architect, develop, test, and deploy generative-AI solutions (online/offline LLMs, SLMs, TLMs) for domain-specific use cases.
Design and implement agentic AI workflows and orchestration using frameworks such as LangGraph, Crew AI, or equivalent.
Integrate enterprise knowledge bases and external data sources via vector databases and Retrieval-Augmented Generation (RAG).
Build and productionize ingestion, preprocessing, indexing, and retrieval pipelines for structured and unstructured data (text, tables, documents, images).
We are looking for an experienced GenAI Senior Engineer to design, build, and maintain scalable, secure, and production-grade AI platforms and applications.
Compensation
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Fractal, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is: $120,000-$150,000. In addition, for the current performance period, you may be eligible for a discretionary bonus.
Responsibilities
Design, develop, and maintain scalable, secure, and highly available GenAI platforms and AI-powered applications on AWS.
Architect and implement Agentic AI solutions, multi-agent systems, tool integrations, function calling, and workflow orchestration frameworks.
Design and implement enterprise-grade RAG solutions using embeddings, vector databases, semantic search, reranking, and knowledge retrieval techniques.
Build AI applications leveraging Amazon Bedrock for LLM inference, model orchestration, prompt management, and foundation model integrations.
Develop reusable AI platform components, SDKs, APIs, accelerators, and reference architectures.
Lead the design, development and operationalisation of GenAI models, intelligent agents, copilots and automation solutions across Bupa.
Responsibilities
Lead the design, development and operationalisation of GenAI models, intelligent agents, copilots and automation solutions across Bupa.
Own AI solution delivery end-to-end, from discovery and scoping through to evaluation, build, testing, deployment, monitoring and optimisation.
Oversee technical solution design, integrations, APIs, data flows and model selection, ensuring alignment with Bupa’s AI and architecture standards.
Manage key elements of the AI engineering stack, including LLM platforms, orchestration frameworks, vector databases, retrieval pipelines, security controls and cloud infrastructure.
Partner with business teams to understand their needs and ensure AI solutions deliver measurable value and improved operational outcomes.
A Technical Product / Program Lead to build the next generation of commercial knowledge capability, moving from a traditional repository to an AI-enabled knowledge ecosystem. The role sits at the intersection of Knowledge Management, Generative AI, automation, information architecture, and technical product leadership.
Responsibilities
Define KPIs for content accuracy, freshness, ownership, review adherence, reliability and AI readiness.
Establish a baseline showing how much of current knowledge can be trusted and where intervention is required.
Measure and advance content review and governance automation, reducing manual effort.
Create actionable reporting identifying healthy, stale, unowned, or unreliable content.
Define the architecture and roadmap for agentic capabilities.
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