Software Engineer Agentic AI
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Job Description
- Lead the design and development of agentic AI systems, leveraging modern frameworks such as LangChain, LangGraph, CrewAI, and similar orchestration libraries to build autonomous, goal‑driven AI workflows.
- Drive hands‑on development of multi‑agent systems, including agent collaboration, task delegation, memory management, and tool integration patterns.
- Act as a hands‑on expert in Python‑based AI/ML development, using industry‑standard libraries such as NumPy, Pandas, Scikit‑learn, TensorFlow, PyTorch, Keras, and associated tooling.
- Apply strong fundamentals in machine learning, deep learning, and data processing, ensuring models and agents are explainable, testable, and maintainable.
- Guide teams on model lifecycle management, inference optimisation, and production readiness of AI solutions.
- Lead integration of Large Language Models (LLMs) into enterprise systems, including prompt engineering, tool‑calling, retrieval‑augmented generation (RAG), and agent reasoning strategies.
- Ensure AI solutions align with responsible AI principles, data privacy, security, and regulatory expectations.
- Evaluate and influence adoption of latest AI innovations, including Speculative AI techniques, reasoning‑augmented agents, and hybrid symbolic‑LLM approaches.
- Champion modern AI‑assisted engineering practices, including familiarity with tools such as Claude Code, GitHub Copilot, and similar coding assistants, to improve developer productivity and code quality.
- Set standards for engineering quality, testing, observability, and operational readiness in AI‑driven systems.
- Experience working in cloud environments, with hands‑on exposure to AWS Bedrock or similar managed AI platforms.
- Deliver software using Agile methodologies, actively participating in sprint planning, reviews, retrospectives, and continuous improvement.
- Apply DORA metrics (deployment frequency, lead time for change, change failure rate, MTTR) to drive delivery predictability and engineering health.
- Strong familiarity with modern developer tooling including GitLab, DevSecOps pipelines, and secure CI/CD practices.
- Hands‑on experience with: Docker Desktop for local containerized development, IntelliJ IDEA or equivalent enterprise IDEs and Secure source control, branching strategies, and automated quality gates
- Drive a test‑first, quality‑driven engineering culture, with hands‑on experience in – Contract Testing (PACT), Unit Testing (Junit), Performance and Load testing (Jmeter), Mutation Testing.