8 months ago
USA (city not specified)Mid Level
Responsibilities
- Partner with client stakeholders to understand requirements, constraints, and business objectives.
- Design and implement custom AI systems, including model integrations, data pipelines, APIs, and deployment infrastructure.
- Build and deploy ML/AI solutions and integrate LLMs and generative models into client products and workflows.
- Support model fine-tuning, prompt engineering, and evaluation pipelines where applicable.
- Collaborate with product, design, and research teams to develop reusable components and improve service delivery.
- Advise client technical teams on AI/ML development, deployment, infrastructure, architecture, security, and scalability.
- Deliver hands-on workshops, documentation, and training for client teams.
Requirements
- At least 2 years of software engineering experience, ideally in full-stack or backend-focused roles.
- Hands-on experience delivering real-world ML/AI projects independently or with data science teams.
- Strong programming skills with Python required; JavaScript, TypeScript, Go, or similar experience is a plus.
- Comfort with AWS, GCP, or Azure and CI/CD workflows.
- Excellent communication and client interaction skills.
- Preferred experience with LLMs such as OpenAI, Anthropic, or Cohere; vector search; or prompt engineering.
- Preferred consulting, professional services, or customer-facing technical experience.
- Preferred familiarity with MLOps tools such as MLflow, Weights & Biases, or SageMaker.
- Knowledge of enterprise security, data privacy, and compliance constraints is preferred.
Benefits
- Competitive compensation including salary plus deployment bonuses or client uplift incentives.
- Equity options.
- Flexibility to work across industries and problem domains.
- Collaborative, mission-driven team focused on real-world AI impact.
Tech Stack
Categories
Forward Deployed
About Eliza
Eliza is a New York–based technology services firm that designs and delivers enterprise AI solutions. The company helps organizations adopt ChatGPT Enterprise, build custom LLM integrations and agents, and implement predictive analytics and intelligent automation. It operates on a consulting and implementation model, partnering across industries to move prototypes into production; privately held with a headquarters in New York City.