Required Skills and Experience
• Bachelor’s degree in computer science, Engineering, or equivalent.
• Hands-on experience with Python development and frontend UI technologies (e.g., TypeScript, React.js, etc.) to build demos/systems from scratch as a full stack engineer.
• Hands-on experience building AI based solutions using AI frameworks such as LangChain, Microsoft Semantic Kernel, Google ADK or Microsoft Agent Framework.
• Knowledge of LLMs, AI Agent architectures, Agent Telemetry/Observability frameworks (Langsmith, Langfuse, litellm etc).
• Expertise with Docker, Kubernetes, and at least one cloud platform (Azure, AWS, GCP) or on premises.
• Experience in microservices-based architectures.
• Solid grasp of the software delivery lifecycle, version control (Git & GitHub), and data engineering tools such as Pandas and Spark.
• Experience with cloud AI platforms (AWS, Azure, Google AI) and distributed computing architectures.
• Ability to translate business requirements into technical solutions and communicate technical value to diverse stakeholders, including executive audiences.
• Hands-on experience with SQL (e.g., PostgreSQL), NoSQL (e.g., MongoDB), and vector databases for agent data storage, semantic queries and retrieval.
• Proficiency in CI/CD (e.g., GitHub Actions), automated testing, and observability.
• Familiarity with agent-based modeling, multi-agent systems, or reinforcement learning.
• Proficiency in API development, backend services, and cloud platforms (AWS, Azure, GCP).
• Practical knowledge of deploying RAG architectures and integrating structured and unstructured knowledge sources into AI solutions.
• Open-Source Ecosystems: Familiarity with community-driven AI tools and libraries, including Hugging Face and relevant repositories.
• T-shaped Profile: Deep technical expertise in one or two domains, with broad understanding across AI/ML, cloud, and consulting.
• Agentic AI Systems: Experience designing, building, or integrating multi-agent systems and orchestration frameworks (e.g., LangGraph, Semantic Kernel, Agent Framework, AutoGen, CrewAI), including the development of agent protocols and coordination mechanisms.
• Performance & Security: Knowledge of system-level optimisation and security best practices for scalable AI systems.
• Willingness to travel up to 25% globally.