Sr. Software Engineer (AI/Gen AI, ETL, Cloud & Devops)

About the role

We are building Client’s founding Forward Deployment Engineering team, and we're looking for seasoned engineers to set the bar.

Responsibilities

• Embed with internal teams and external customers to understand their workflows, pain points, and goals — then design and deploy solutions that fit.
• Build and deploy production-quality agentic AI systems (voice agents, chat agents, copilots, automation workflows) alongside data integrations and custom software to solve concrete business problems.
• Design and implement RAG pipelines, prompt orchestration layers, and multi-agent workflows tailored to real operational environments and BU-specific data.
• Prototype rapid proofs-of-concept, iterate based on direct user feedback, and move them into reliable, scalable production — on a weekly cadence, not quarterly.
• Integrate data pipelines, APIs, and third-party systems, handling the real-world messiness of enterprise environments.
• Partner with end users and stakeholders — running demos, gathering requirements, troubleshooting live, and translating technical concepts into business value.
• Feed recurring pain points and friction back to product and engineering teams to influence the core roadmap.
• Monitor and improve solutions for reliability, performance, and adoption after launch — you own outcomes, not just deliverables.
• Establish the playbooks, standards, and tooling for the FDE function, and mentor junior engineers as the team grows.

Requirements

Experience: 8+ years building and shipping software in production, including significant time owning complex, ambiguous projects end-to-end with minimal supervision.

Technical depth: Python is required as your primary language; strong proficiency in at least one additional language (JavaScript/TypeScript, Java, or Go).

Deep, hands-on programming expertise, strong system design judgment, and fluency working across the full stack and with APIs.

AI/GenAI engineering (required): Demonstrated hands-on experience deploying LLM-based systems in production — prompt engineering, RAG architecture, agent orchestration (LangChain, LlamaIndex, or equivalent).

You have shipped agentic AI to real users, not just prototyped it.

Data platform fluency: Proven experience with data tools — complex SQL, ETL/ELT pipelines, data warehouses (Snowflake, Databricks, or equivalent).

Ability to wrangle and normalize enterprise datasets for AI system consumption.

Cloud & DevOps (required): Comfortable deploying to AWS, GCP, or Azure; working with Docker and CI/CD pipelines.

You can ship to cloud without hand-holding.

Customer-facing track record: A track record of customer-facing or forward-deployed work — you've shipped solutions directly with end users, navigated enterprise complexity, and earned the trust of senior stakeholders.

Leadership: You've led projects, set technical direction, and raised the bar for those around you — formally or informally.

Communication: Exceptional written and verbal communication; you translate fluidly between deep technical detail and executive-level business value.

Bias for action: You ship, learn, and iterate quickly at a high quality bar, and you bring order to ambiguous, fast-moving situations.

Qualifications

Experience: 8+ years building and shipping software in production, including significant time owning complex, ambiguous projects end-to-end with minimal supervision.

Technical depth: Python is required as your primary language; strong proficiency in at least one additional language (JavaScript/TypeScript, Java, or Go).

Deep, hands-on programming expertise, strong system design judgment, and fluency working across the full stack and with APIs.

AI/GenAI engineering (required): Demonstrated hands-on experience deploying LLM-based systems in production — prompt engineering, RAG architecture, agent orchestration (LangChain, LlamaIndex, or equivalent).

You have shipped agentic AI to real users, not just prototyped it.

Data platform fluency: Proven experience with data tools — complex SQL, ETL/ELT pipelines, data warehouses (Snowflake, Databricks, or equivalent).

Ability to wrangle and normalize enterprise datasets for AI system consumption.

Cloud & DevOps (required): Comfortable deploying to AWS, GCP, or Azure; working with Docker and CI/CD pipelines.

You can ship to cloud without hand-holding.

Customer-facing track record: A track record of customer-facing or forward-deployed work — you've shipped solutions directly with end users, navigated enterprise complexity, and earned the trust of senior stakeholders.

Leadership: You've led projects, set technical direction, and raised the bar for those around you — formally or informally.

Communication: Exceptional written and verbal communication; you translate fluidly between deep technical detail and executive-level business value.

Bias for action: You ship, learn, and iterate quickly at a high quality bar, and you bring order to ambiguous, fast-moving situations.