• Drive Technical Excellence: Act as a technical pillar within the Lab, implementing high-standard code and sophisticated system designs. You will mentor mid-level peers and lead deep-dive code reviews. • Architect Multi-Agent Systems: Design and deploy distributed systems and multi-agent architectures that automate complex engineering tasks. You will own the architectural decisions for "build vs. integrate" strategies. • Innovate with Agentic AI: Spearhead the evaluation and prototyping of LLMs, Agentic frameworks, and Model Context Protocols (MCPs). You will transform theoretical AI advancements into production-ready tools. • Own the Full Lifecycle: Take responsibility for the entire development cycle. From initial concept and API integration to production deployment and long-term scalability. • Influence Product Strategy: Partner with Product and Engineering Managers to ensure the Lab’s innovations align with the broader company roadmap and provide measurable ROI to our developers.

Role Description As a Founding Applied AI Engineer at Valent, you will help design and build the core intelligence layer of the product. This role is focused on applying modern AI techniques to messy enterprise problems involving documents, retrieval, reasoning, workflow automation, and human-in-the-loop review. - Help build systems that extract structured information from supplier documents, specifications, certificates, declarations, questionnaires, audit evidence, and regulatory files. - Power AI-assisted workflows for document collection, evidence packet assembly, customer requests, and audit preparation. - Use retrieval, ranking, and document understanding to help users find the right evidence across fragmented sources. - Generate draft responses, summaries, checklists, and compliance artifacts that remain reviewable and auditable by humans. - Build evaluation pipelines to measure quality, reduce hallucination, and improve reliability over time. - Design AI workflows that know when to act autonomously and when to escalate to a human. - Integrate AI features into a polished enterprise product experience. Qualifications - Strong software engineering fundamentals and experience shipping production systems. - Hands-on experience building with LLMs, retrieval systems, document AI, agents, or applied machine learning systems. - Ability to take AI systems from prototype to production. - Comfortable working across the stack when needed. - Ability to write clean, maintainable code and make sound technical tradeoffs. - Care deeply about reliability, observability, testing, and product quality. - Ability to reason from first principles about when AI should automate, assist, or stay out of the way. - Interest in solving messy operational problems, not just building polished demos. - Clear communication in writing and comfort working remotely. - Ability to overlap for part of the day with U.S. Eastern Time. - Desire to join early and help shape the foundation of a company. Requirements - Experience with LLM application development. - Familiarity with RAG systems and vector databases. - Experience in document parsing, OCR, extraction, or classification. - Knowledge of evaluation frameworks for AI systems. - Experience in workflow automation or agentic systems. - Background in enterprise SaaS. - Familiarity with compliance, supply chain, manufacturing, regulatory, or quality workflows. - Experience with React, TypeScript, Node.js, Python, Postgres, or cloud infrastructure. Benefits - Work remotely from India on a U.S.-based enterprise AI product. - Build AI into workflows where the value is operational, measurable, and immediate. - Join early enough to have meaningful influence over architecture, product, and culture. - Work on hard problems at the intersection of AI, enterprise workflow, compliance, and manufacturing. - Help define what modern compliance operations software should look like.

Role Description This role is about engineers who build the AI features our users actually interact with — chat interfaces, agents, retrieval systems, AI-powered workflows inside our product. We're hiring a full stack engineer who can ship LLM-powered features end-to-end: from the prompt and the eval suite, through the retrieval and orchestration layer, to the UI a user clicks. You should be the kind of person who has gone deep on LLMs, has strong opinions about evals, and rolls their eyes at demos that don't survive contact with real users. If your strengths are general full stack engineering rather than LLM-specific work, see also our Full Stack Engineer role — it may be a closer fit. If "AI feature" still means "ChatGPT wrapper" to you, this isn't your team. If you've already shipped something into production and watched it break in interesting ways, keep reading. You'll own AI-powered features end-to-end — model choice, prompts, retrieval, tool use, the orchestration around it, the UI on top, and the evals that keep it from regressing. The work spans research-flavoured experimentation and hard product engineering, often in the same week. Concretely, in your first six months you'd expect to: - Ship at least one significant AI feature into production — agent, RAG flow, generative UI, or similar - Build out our eval and observability stack so we can ship LLM changes with confidence rather than vibes - Drive a measurable improvement on a key model-quality metric (accuracy, latency, cost, hallucination rate) - Help shape our internal AI engineering practices — model selection, prompt versioning, regression testing Day-to-day, you'll write code (a lot of it AI-assisted), design and evaluate prompts, debug agent failures, partner with product and design on what AI features should even be, and make calls on trade-offs between cost, latency, and quality. Qualifications - A genuine willingness to learn. - Adaptability across stacks. - Strong fundamentals. - System design at both altitudes. - Sharp problem-solving. - Hands-on experience building AI features in production. - Evals as a first-class skill. - Production realism about LLM features. - Strong full stack engineering chops. - Comfortable with both database paradigms. - Production cloud experience. - Fluency with AI-assisted development. - High autonomy. Requirements - Real features that real users hit, with real consequences when they fail. - Experience with several of: - LLM APIs (Anthropic, OpenAI, or open-weight models via vLLM/Bedrock/Together) and have opinions on which to use when - Retrieval-augmented generation: chunking strategies, embeddings, vector stores (pgvector, Pinecone, Weaviate, Qdrant), hybrid search - Agentic systems: tool use, multi-step workflows, planning, MCP, frameworks like LangGraph/Inngest/Temporal or your own orchestration - Structured output, function calling, and JSON-mode reliability - Streaming responses and the UX patterns that make them feel good - Prompt engineering as a serious discipline — versioning, A/B testing, regression suites, not vibes - Production realism about LLM features. - Strong full stack engineering chops. - Comfortable with both database paradigms. - Production cloud experience. - Fluency with AI-assisted development. - High autonomy. Nice to have - Fine-tuning or post-training experience (LoRA, RLHF, DPO) — even small-scale - Built or contributed to open-source AI infra, agent frameworks, or eval tooling - Experience with model routing, prompt caching, or other cost/latency optimizations at scale - Worked on AI safety, red-teaming, prompt injection defence, or content moderation pipelines - Background in IR, NLP, or applied ML before the LLM era - Strong writing — this team values it, and AI features live or die by precise language - Prior experience in a small, fast-moving team (under ~30 engineers) Got questions? You can email us at talentsupport@xplortechnologies.com

• Design, build, and implement an end-to-end AI-powered tender response platform. • Develop and maintain backend services and AI workflows using AWS-native technologies. • Build and manage infrastructure using Terraform and cloud-native best practices. • Implement AI and retrieval workflows leveraging AWS Bedrock and vector database technologies such as PGvector. • Design and orchestrate scalable workflows using AWS Step Functions. • Collaborate closely with senior technical stakeholders to deliver project milestones within tight timelines. • Contribute to architecture decisions, backend integrations, and deployment workflows. • Ensure solutions are scalable, maintainable, and production-ready. • Troubleshoot and resolve technical issues across infrastructure, backend services, and AI components. • Support testing, deployment, and optimisation activities throughout the project lifecycle. • Operate effectively in a fast-paced environment with high ownership and minimal oversight.