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

ManpowerGroup Global, Inc.; Overland Park (KS); Hybrid; USD 140,000 - 210,000; Full time

Job summary

ManpowerGroup Global, Inc. is building a Forward Deployment Engineering team in a hybrid setup across multiple locations. The Sr.

Software Engineer will deploy production-grade AI systems, data integrations, and automation workflows while partnering with internal teams and external customers to deliver measurable business outcomes. The role emphasizes hands-on development, rapid iteration, and leadership across projects, with travel up to 25% and a 6–12 month contract that could convert to

Qualifications

• 8+ years of software development in production environments.
• Hands-on AI/GenAI experience with production deployments.
• Strong data/ETL and data pipeline comprehension.
• Cloud deployment experience (AWS/GCP/Azure) and CI/CD familiarity.
• Proven ability to work with customers and stakeholders.

Responsibilities

• Embed with internal teams and customers to understand workflows and deploy solutions.
• Build and deploy production-grade agentic AI systems and data integrations.
• Design RAG pipelines and multi-agent workflows for real environments.
• Prototype quickly based on user feedback and move to scalable production.
• Collaborate with product and engineering to guide roadmap.
• Mentor junior engineers as the team scales.
• Monitor reliability, performance, and adoption after launch.

Skills

Python

JavaScript/TypeScript

Java

Go

AI/GenAI engineering

Data platform & ETL

Cloud & DevOps

Customer-facing

Leadership

Communication

Bias for action

Job description

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

Bellevue, WA / Overland Park, KS / Frisco, TX (Hybrid). Travel: up to 25%. 6–12-month contract with potential conversion to full-time.

We are building Client’s founding Forward Deployment Engineering team, and we're looking for seasoned engineers to set the bar. As a Senior/Staff FDE, you sit at the intersection of engineering and the customer. You embed directly with internal business teams and external partners to deploy production agentic AI systems — voice agents, copilots, automation workflows, data integrations, and custom solutions — that deliver measurable business outcomes in live operational environments. You are part builder, part problem‑solver, and part trusted advisor: writing production code one day, mapping a messy real‑world workflow the next, and advising senior stakeholders on strategy by the end of the week. Success is measured by whether business outcomes actually move — not by deployments shipped or tickets closed. This is a high‑impact role for an experienced engineer who has done the work before, thrives in ambiguity, and operates with little oversight. As one of the first FDEs, you won’t just deploy solutions — you’ll define how this team works: the playbooks, the standards, the tooling, and the bar for everyone who follows. You’ll also mentor more junior engineers as the team scales.

What You’ll Do

• 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.

What You’ll Bring

• 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.

Nice to Have

• MLOps fundamentals: model versioning, A/B testing frameworks, monitoring and observability tooling.
• Vector database experience in a production RAG context (Pinecone, Milvus, pgvector, or equivalent).
• Experience deploying AI/ML solutions across multiple enterprise domains (customer care, retail, supply chain, finance).
• Telecommunications or large‑scale enterprise data domain knowledge.
• Experience as a founding or early member of a new team or function.