Principal AI Engineer

About the Role :

1WRK by ANSR is looking for a Principal AI Engineer to lead the design and delivery of intelligent, production-grade AI systems across our platform. This is a hands-on technical leadership role for someone who moves comfortably across data science, machine learning, product engineering, cloud infrastructure, and generative AI, and who can turn that breadth into shipped, reliable products. You will set technical direction for AI initiatives, build and ship models and platforms yourself, and mentor and guide a small pod of engineers and data scientists.

What You'll Do :

- Technical leadership : Own the technical vision, architecture, and roadmap for AI/ML and GenAI initiatives, translating ambiguous business problems into well-scoped, buildable technical plans.

- End-to-end ML & AI delivery : Design, build, and deploy machine learning models, LLM-based applications, and data products, from exploration and experimentation through to production, monitoring, and iteration.

- GenAI systems : Architect and build GenAI capabilities such as RAG pipelines, agentic workflows, prompt and evaluation frameworks, and fine-tuning/adaptation of foundation models, with a clear eye on cost, latency, and reliability.

- Product engineering : Partner closely with product and engineering teams to embed AI capabilities into customer-facing features, defining APIs, data contracts, and system interfaces that scale.

- Cloud & MLOps on AWS : Design and operate scalable, secure, and cost-efficient ML/AI infrastructure on AWS (e.g., SageMaker, Bedrock, Lambda, ECS/EKS, S3, Step Functions), including CI/CD for models and robust monitoring and observability.

- Data foundations : Guide data pipeline, feature engineering, and data quality decisions that ML and GenAI systems depend on, working closely with data engineering.

- Team leadership & mentorship : Lead a small pod of AI/ML engineers and data scientists, setting technical direction, reviewing designs and code, unblocking delivery, and growing the team's skills, while remaining a hands-on contributor.

- Responsible AI : Champion best practices in model evaluation, bias and fairness checks, security, and governance for AI systems deployed in production.

- Cross-functional influence : Act as the go-to technical voice on AI capabilities for leadership, product, and business stakeholders, communicating trade-offs and recommendations clearly.

What We're Looking For :

- Experience in data science, machine learning, or software/product engineering, with demonstrated depth in at least two of these areas and working knowledge across all of them.

- Proven track record designing, building, and shipping ML models and data-driven products into production at scale, not just in notebooks or proof-of-concepts.

- Hands-on experience with Generative AI / LLM-based systems : RAG, agents, prompt engineering, evaluation, fine-tuning, or building applications on top of foundation models (OpenAI, Anthropic, Bedrock, or open-source).

- Strong software/product engineering fundamentals: clean API design, testing, code review, CI/CD, and experience shipping features that real users depend on.

- Deep, practical experience with AWS : comfortable architecting and operating solutions using services such as SageMaker, Bedrock, Lambda, ECS/EKS, S3, RDS/DynamoDB, and Step Functions.

- Strong Python skills, and familiarity with standard ML/DS tooling (e.g., PyTorch/TensorFlow, scikit-learn, pandas, MLflow or similar).

- Solid grounding in statistics, applied ML, and experimentation (A/B testing, offline/online evaluation).

- Experience mentoring engineers or data scientists and providing technical leadership, with or without formal people-management responsibility.

- Excellent communication skills: able to explain technical trade-offs to both engineers and non-technical stakeholders and to write clear technical documentation.

Nice to Have :

- Experience in SaaS, HR-tech, or global workforce/EOR platforms.

- Exposure to vector databases, knowledge graphs, or semantic search.

- Experience with data governance, model risk, or AI compliance frameworks.

- Contributions to open-source ML/AI projects, patents, or publications.

- Prior experience as a technical lead, staff/principal engineer, or engineering manager for a small team.

What Success Looks Like :

- AI/ML and GenAI features you've architected are live in production, reliable, and measurably improving product or business outcomes.

- The AI engineering pod you guide ships faster and with higher quality because of the standards, reviews, and mentorship you provide.

- Leadership and product teams treat you as the trusted technical authority on what's possible with AI, and at what cost and risk.

Why Join 1WRK by ANSR :

You'll have the mandate to shape how AI is built and deployed across the platform: real technical breadth across the stack, genuine ownership of outcomes, and a direct line to product and business impact.