About the job

• Work in a scaled Agile working environment
• Be 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 habits
• Use 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 systems
• Mentor 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 Responsibilities

• Design 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 Qualifications

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 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-Have

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.