Forward Deployed Engineer

Partner directly with client stakeholders to understand requirements, constraints, and business objectives.

Lead the technical design and hands-on implementation of custom AI systems—including model integration, data pipelines, APIs, and deployment infrastructure.

Rapidly prototype and iterate with clients in live environments.

Build and deploy ML/AI solutions using technologies like Python, TensorFlow/PyTorch, LangChain, and cloud-native tools.

Integrate LLMs and other generative models into client products and workflows.

Support model fine-tuning, prompt engineering, and evaluation pipelines where applicable.

Work with internal teams (product, design, research) to shape reusable components and frameworks based on deployment experiences.

Contribute client feedback and frontline insights to improve service delivery and product strategy.

Advise client technical teams on best practices for AI/ML development and deployment.

Deliver hands-on workshops, documentation, and training to enable long-term client success.

Guide clients through infrastructure and architecture decisions (e.g., cloud, security, scalability).

2+ years of software engineering experience, ideally in full-stack or backend-focused roles.

Hands-on experience delivering real-world ML/AI projects, either independently or in collaboration with data science teams.

Strong programming skills (Python required; familiarity with JavaScript/TypeScript, Go, or similar a plus).

Comfort with modern cloud platforms (AWS, GCP, or Azure) and CI/CD workflows.

Excellent communication and client interaction skills.

Experience with LLMs (e.g., OpenAI, Anthropic, Cohere), vector search, or prompt engineering.

Prior consulting, professional services, or customer-facing technical roles.

Familiarity with MLOps practices and tools (e.g., MLflow, Weights & Biases, SageMaker).

Knowledge of common enterprise security, data privacy, and compliance constraints.