Associate AI Engineer - Builders Program Tamara 

We re looking for a fresh graduate or early-career AI Engineer on a builder track.

This role sits between software engineering and applied AI. You will build AI-powered features end-to-end: from turning a product problem into an AI approach, to building pipelines and evaluation, to shipping reliable services in production.

You ll work with both classical ML and generative AI (LLMs, RAG, agents) where they make sense. With the rapid pace of AI, we care more about fundamentals than buzzwords. Use AI assistants to move faster, but always own correctness, privacy, safety, and user impact.

Your responsibilities

• Build AI products that ship
  • Design and implement AI-powered features and internal tools.
  • Integrate models into real systems (APIs, workflows, dashboards, and operational processes).
• Work with LLMs in a production-ready way
  • Build retrieval-augmented generation (RAG) pipelines: data sourcing, chunking, embeddings, retrieval, and prompt templates.
  • Build agent-style workflows when needed (tool use, guardrails, and deterministic fallbacks).
• Own evaluation and quality
  • Define what good means: success metrics, offline tests, and human-in-the-loop review.
  • Set up evaluations for quality, safety, latency, and cost.
• Make AI reliable and safe
  • Add observability: logging, tracing, and monitoring for quality regressions.
  • Implement privacy and security controls (PII handling, access control, redaction where needed).
  • Participate in incident response and postmortems when AI systems misbehave.
• Partner across teams
  • Work with product, data, design, and engineering stakeholders to translate messy problems into measurable solutions.
  • Collaborate with platform teams to use existing data pipelines, event streams, and AI tooling responsibly.
• Use AI tools thoughtfully
  • Use AI assistants for prototyping, debugging, and documentation.
  • Validate outputs, document assumptions, and protect sensitive data.

Your expertise (must have)

• Fresh graduate or of relevant experience (internships and projects count).
• Solid programming fundamentals in Python (preferred) or another language used for backend services.
• Strong foundations in:
  
  • Software engineering basics (APIs, testing, reliability)
  • Data handling (SQL and/or dataframes)
  • ML/AI fundamentals (training vs. inference, evaluation, overfitting intuition)
• Clear communication and a collaborative approach.

Nice to have

• Hands-on experience with LLMs and/or RAG from projects (LangChain/LlamaIndex or similar patterns).
• Familiarity with model serving and deployment (REST/gRPC services, batch jobs, streaming consumers).
• Exposure to MLOps concepts (experiment tracking, model registry, monitoring).
• Familiarity with vector databases, search, or information retrieval.
• Understanding of responsible AI, privacy, and security (PII handling, access control, prompt injection awareness).
• Experience using AI assistants responsibly for coding and analysis.

What success looks like

• You ship at least one AI feature or internal tool that is used by real users.
• You set up a simple evaluation and monitoring loop so the system improves over time.
• Your solutions balance quality, latency, and cost, with clear tradeoffs.
• When the AI output is wrong or risky, you can debug, mitigate, and explain why.