AI Engineer – Enterprise Agent Development

Join the Team Revolutionizing Procurement Analytics at SenseCloud

Imagine working at a company where you get the best of all worlds: the fast-paced execution of a startup and the guidance of leaders who've built things that actually work at scale. We're not just rethinking how procurement analytics is done — we're redefining them.

At Sensecloud, we envision a future where Procurement data management and analytics is as intuitive as your favorite app. No more complex spreadsheets, no more waiting in line to get IT and analytics teams' attention, no more clunky dashboards —just real-time insights, smooth automation, and a frictionless experience that helps companies make fast decisions. If you're ready to help us build the future of procurement analytics, come join the ride.

You'll work alongside the brightest minds in the industry, learn cutting-edge technologies, and be empowered to take on challenges that will stretch your skills and your thinking. If you're ready to help us build the future of procurement, analytics come join the ride.

About the Role

We're looking for an AI Engineer who can design, implement, and productionize LLM-powered agents that solve real-world enterprise problems—think automated research assistants, data-driven copilots, and workflow optimizers. You'll own projects end-to-end: scoping, prototyping, evaluating, and deploying scalable agent pipelines that integrate seamlessly with our customers' ecosystems.

What you'll do:

- Architect & build multi-agent systems using frameworks such as LangChain, LangGraph, AutoGen, Google ADK, Palantir Foundry, or custom orchestration layers.
- Fine-tune and prompt-engineer LLMs (OpenAI, Anthropic, open-source) for retrieval-augmented generation (RAG), reasoning, and tool use.
- Integrate agents with enterprise data sources (APIs, SQL/NoSQL DBs, vector stores like Pinecone, Elasticsearch) and downstream applications (Snowflake, ServiceNow, custom APIs).
- Own the MLOps lifecycle: containerize (Docker), automate CI/CD, monitor drift & hallucinations, set up guardrails, observability, and rollback strategies.
- Collaborate cross-functionally with product, UX, and customer teams to translate requirements into robust agent capabilities and user-facing features.
- Benchmark & iterate on latency, cost, and accuracy; design experiments, run A/B tests, and present findings to stakeholders.
- Stay current with the rapidly evolving GenAI landscape and champion best practices in ethical AI, data privacy, and security.

Must-Have Technical Skills

- 3–5 years software engineering or ML experience in production environments.
- Strong Python skills (async I/O, typing, testing) plus familiarity with TypeScript/Node or Go a bonus.
- Hands-on with at least one LLM/agent frameworks and platforms (LangChain, LangGraph, Google ADK, LlamaIndex).
- Solid grasp of vector databases (Pinecone, Weaviate, FAISS) and embedding models.
- Experience building and securing REST/GraphQL APIs and microservices.
- Cloud skills on AWS, Azure, or GCP (serverless, IAM, networking, cost optimization).
- Proficient with Git, Docker, CI/CD (GitHub Actions, GitLab CI, or similar).
- Knowledge of ML Ops tooling (Kubeflow, MLflow, SageMaker, Vertex AI) or equivalent custom pipelines.

Core Soft Skills

- Product mindset: translate ambiguous requirements into clear deliverables and user value.
- Communication: explain complex AI concepts to both engineers and executives; write crisp documentation.
- Collaboration & ownership: thrive in cross-disciplinary teams, proactively unblock yourself and others.
- Bias for action: experiment quickly, measure, iterate—without sacrificing quality or security.
- Growth attitude: stay curious, seek feedback, mentor juniors, and adapt to the fast-moving GenAI space.

Nice-to-Haves

- Experience with RAG pipelines over enterprise knowledge bases (SharePoint, Confluence, Snowflake).
- Hands-on with MCP servers/clients, MCP Toolbox for Databases, or similar gateway patterns.
- Familiarity with LLM evaluation frameworks (LangSmith, TruLens, Ragas).
- Familiarity with Palantir/Foundry.
- Knowledge of privacy-enhancing techniques (data anonymization, differential privacy).
- Prior work on conversational UX, prompt marketplaces, or agent simulators.
- Contributions to open-source AI projects or published research.

Why Join Us?

- Direct impact on products used by Fortune 500 teams.
- Work with cutting-edge models and shape best practices for enterprise AI agents.
- Collaborative culture that values experimentation, continuous learning, and work–life balance.
- Competitive salary, equity, remote-first flexibility, and professional development budget.

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