Gen AI Analyst

We’re seeking a hands-on Generative AI Analyst with a background in Software, Data, or Machine Learning Engineering.

You’ll design, build, and deploy generative-AI-driven solutions focused on real-world applications (i.e., no research-only roles).

You’ll work closely with engineering teams to implement practical AI capabilities using LLMs and RAG setups.

Job type: Remote

Key Responsibilities:

• Model Customization & RAG: Implement retrieval-augmented generation techniques to customize LLMs for practical business use.
• API & Platform Integration: Use AWS Bedrock, OpenAI or similar APIs to embed generative AI into existing systems.
• Applied Solution Development: Build AI-powered tools to enhance operational efficiency, decision-support systems, or customer workflows in industry settings.
• Data Prep & Collaboration: Work with data engineering to preprocess and manage data for model inputs, ensuring security and compliance.
• Performance Tuning & Production Deployment: Monitor and refine LLM deployments in scalable, reliable environments.
• Cross-Functional Partnership: Collaborate with software engineers, product managers, and stakeholders to deliver AI solutions that meet real needs.
• Documentation & Communication: Create clear documentation and explain technical concepts to both technical and non-technical audiences in a hands-on context.

We’re Looking For:

• Background: 1–3 years (or more) of applied experience in Software, Data, or ML Engineering (e.g., backend, data pipelines, model implementation).
• Technical Fluency: Strong Python skills and experience with ML frameworks (TensorFlow, PyTorch, scikit‑learn).
• Cloud Experience: Familiarity with AWS, GCP, or Azure integration.
• Generative AI Passion: Interest in LLMs, prompt engineering, RAG, and applied AI—demonstrated through project work or prior deployments.
• Problem-Solving & Ownership: Ability to take a project from prototype to delivery, optimizing for performance and business value.
• Soft Skills: Clear communication, cross-functional collaboration, agile mindset.
• Education: Bachelor’s or Master’s in Computer Science, Engineering, Data Science—PhD not required.

Pluses (Nice to Have)

• Experience with LLM fine-tuning, prompt engineering, or LangChain/Agent frameworks.
• Familiarity with MLOps tools (e.g., MLflow, Docker, CI/CD pipelines).
• Industry-specific experience (e.g. maritime, logistics, finance) is a bonus—but we prioritize applied engineering experience over domain knowledge.