AI Tech Lead

Eurisko is looking for an experienced AI Tech Lead to lead the technical design and delivery of enterprise AI solutions from prototype to production.

This is a hands‑on technical leadership role for someone who understands both modern Generative AI / LLM technologies and traditional AI/ML engineering
, and can turn business requirements into secure, scalable, production‑ready systems.

• Lead the architecture, design, and technical implementation of enterprise AI solutions.
• Own AI projects technically from experimentation and proof‑of‑concept through production deployment.
• Design solutions using LLMs, Generative AI, RAG, AI agents, NLP, machine learning, and intelligent automation
  .
• Evaluate and select appropriate models, frameworks, vector databases, orchestration tools, and AI platforms.
• Design scalable APIs and backend services for integrating AI capabilities into web, mobile, and enterprise systems.
• Establish best practices around prompt engineering, retrieval, model evaluation, observability, security, and cost optimization.
• Work closely with Data Scientists, AI Engineers, Backend Engineers, Dev Ops teams, Product Managers, and business stakeholders.
• Lead technical discussions with clients and translate complex business problems into practical AI architectures.
• Review code and architecture and mentor AI and software engineers.
• Ensure AI solutions meet enterprise requirements for security, privacy, performance, scalability, and reliability.
• Stay current with developments across the rapidly evolving AI ecosystem and determine which technologies are genuinely suitable for production use.

• 7+ years of software engineering / AI experience
  , including strong recent experience building production AI solutions.
• Strong hands‑on development skills, preferably in Python and/or modern backend technologies.
• Deep understanding of LLMs and Generative AI architectures
  .
• Hands‑on experience with areas such as:
• Retrieval‑Augmented Generation (RAG)
• Vector databases and embeddings
• AI agents and tool/function calling
• Prompt engineering and evaluation
• LLM orchestration frameworks
• NLP and machine learning
• Model APIs and open‑source models
• Strong understanding of APIs, microservices, distributed architectures, and cloud‑native applications.
• Experience deploying AI workloads on cloud platforms such as AWS, Azure, or GCP.
• Understanding of MLOps / LLMOps, CI/CD, monitoring, model evaluation, and production observability.
• Strong understanding of AI security, data privacy, access control, and responsible AI considerations.
• Ability to lead technical teams while remaining highly hands‑on.
• Strong communication skills and confidence working directly with enterprise clients and senior stakeholders.

Experience delivering AI solutions within digital banking, fintech, financial services, government, or other large enterprise environments is highly valued.

Experience building production solutions involving enterprise knowledge assistants, intelligent search, conversational AI, document intelligence, personalization, recommendation systems, or AI‑powered workflows would be a strong advantage.