AI Engineer

Education & experience

3+ years of experience as Software Engineer / ML Engineer / AI Engineer, with at least 1-2 years working directly with LLMs in real applications (not just experiments or coursework)

Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field (or equivalent practical experience) Core technical skills Programming & software engineering:

Strong proficiency in Python (core language features, packaging, testing, async, type hints)

Very strong software engineering practices: version control (Git), unit/integration testing, code reviews, CI/CD

Experience building and consuming REST/gRPC APIs and integrating external tools/services Machine Learning (good understanding):

Understanding of core ML concepts: supervised/unsupervised learning, train/validation/test splits, overfitting, regularization, and common metrics (precision, recall, F1, ROC-AUC, etc.)

Good understanding of deep learning basics (neural networks, embeddings) and at least one ML/DL framework (e.g., PyTorch, TensorFlow, JAX, scikit-learn) LLMs & agentic AI (very strong understanding):

Deep practical knowledge of large language models:

Tokenization, context windows, temperature, top-p, system vs user prompts

Prompt engineering patterns (ReAct, chain-of-thought, tool-calling/tool-use)

Fine-tuning / adapters / instruction-tuning, or experience with RAG as an alternative

Experience building LLM-powered applications end-to-end: from idea → prototype → production

Familiarity with safety and reliability considerations: hallucinations, guardrails, content filtering, privacy Agentic frameworks (required understanding, experience preferred):

Conceptual understanding of modern agentic frameworks and patterns (stateful graphs, multi-agent coordination, human-in-the-loop, memory, and evaluation)

Hands-on experience with at least one of: o Google Agent Development Kit (ADK)

building multi-agent workflows, using its orchestration, tools, and evaluation features o LangGraph

designing graph-based, stateful agent workflows with cycles, branches, and durable execution

Candidates must be able to read, reason about, and extend ADK/LangGraph-based codebases

Direct production experience with both ADK and LangGraph is a strong plus Data & infra:

Experience working with vector databases (e.g., Pinecone, Weaviate, pgvector, Chroma) for retrieval-augmented generation

Comfortable with SQL and basic data modeling

Experience deploying on at least one major cloud platform (GCP, AWS, Azure) and using managed services (e.g., serverless runtimes, container orchestration, secrets management) Soft skills:

Ability to translate ambiguous business requirements into concrete technical designs

Strong communication skills; able to explain trade-offs to both technical and non-technical stakeholders

Comfort working in an experimental environment with rapid iteration, but with a strong bias towards production quality and maintainability

Experience with:

Vertex AI / Gemini or other hosted LLM ecosystems

Related frameworks and tools: LangChain, LlamaIndex, semantic search, evaluation frameworks (e.g., RAGAS, custom eval harnesses)

Monitoring and observability stacks (OpenTelemetry, Prometheus/Grafana/NewRelic, Datadog, etc.)

Background in one or more of:

Information retrieval / search

NLP (beyond LLMs): classic text processing, embeddings, semantic similarity

Security & compliance for AI systems (PII handling, access control, audit logging)

Contributions to open-source AI projects, blog posts, or talks about LLMs/agentic systems