• 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.

Some other highly valued skills may include:

• Contributes as an Individual Contributor (ICE) in the delivery of agentic AI solutions, owning end‑to‑end technical implementation of autonomous and multi‑agent systems, including design, development, integration, testing, and production readiness, while operating with minimal supervision and strong engineering accountability.
• Work effectively within Agile/Scrum delivery models, using Jira for backlog management, delivery tracking, and sprint execution.
• Apply strong analytical and troubleshooting skills to diagnose and resolve complex issues in distributed, event‑driven systems.
• Demonstrate excellent verbal and written communication, producing clear architecture designs, decision records, and technical documentation.
• Collaborate closely with cross‑functional teams (engineering, DevOps, security, architecture) to deliver integrated, enterprise‑ready solutions.
• Exhibit strong ownership, self‑drive, and innovation mindset, staying current with cloud‑native technologies and modern engineering best practices.