AI Engineer

Role SummaryWork in a scaled Agile working environmentBe part of a global and diverse team; Contribute to all stages of software development lifecycle;Participate in peer-reviews of solution designs and related code;Maintain high standards of software quality within the team by following good practices and habitsUse frameworks like Google Agent Development Kit (Google ADK) and LangGraph to build robust, controllable, and observable agentic architectures. Assist in the design of LLM-powered agents and multi-agent workflows (planning, tool use, orchestration, memory, and human-in-the-loop)Lead the implementation, deployment and test of multi-agent systemsMentor junior engineers on best practices for LLM engineering and agentic system development.Drive technical discussions and decisions related to AI architecture and framework adoption.Proactively identify and address technical debt and areas for improvement in AI systems.Represent the team in cross-functional technical discussions and stakeholder meetings.Key ResponsibilitiesDesign and build complex agentic systems with multiple interacting agents.Implement robust orchestration logic (state machines / graphs, retries, fallbacks, escalation to humans).Implement RAG pipelines, tool calling, and sophisticated system prompts for optimal reliability, latency, and cost control.Apply core ML concepts to evaluate and improve agent performance, including dataset curation and bias/safety checks.Lead the development of agents using Google ADK and/or LangGraph, leveraging advanced features for orchestration, memory, evaluation, and observability.Integrate with supporting libraries and infrastructure (e.g., LangChain/LlamaIndex, vector databases, message queues, monitoring tools) with minimal supervision.Define success metrics, build evaluation suites for agents (automatic + human evaluation), and drive continuous improvement.Curate and maintain comprehensive prompt/test datasets; run regression tests for new model versions and prompt changes.Deploy and operate AI services in production, establishing CI/CD pipelines, observability, logging, and tracing.Debug complex failures end-to-end, identifying and document root causes across models, prompts, APIs, tools, and data.Work closely with product managers and stakeholders to shape requirements, translate them into agent capabilities, and manage expectations.Document comprehensive designs, decisions, and runbooks for complex systems.Must-Have QualificationsEducation & experience3+ 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 undeerstanding 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:Google Agent Development Kit (ADK) – building multi-agent workflows, using its orchestration, tools, and evaluation features.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.Nice-to-HaveExperience 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.What We OfferOpportunity to work on real-world agentic AI systems with modern frameworks (Google ADK, LangGraph, Vertex AI, etc.).Tight collaboration with a small, senior engineering and product team.Budget for learning (conferences, courses, books) specifically around agentic AI, LLMs, and MLOps.Competitive salary, equity, and flexible/remote work options.