Role : GenAI / AI Engineering

Role Focus : Hands-on technical leadership across GenAI, LLM applications, RAG, AI agents, backend engineering and enterprise cloud architecture.

Experience : 8 - 12+ years overall software engineering experience, including 3+ years of strong hands-on AI/ML, GenAI or related AI engineering experience.

Role Overview :

- Translate client and business problems into practical technical solutions and scalable solution architecture.

- Own the architecture and technical design of AI, GenAI and agentic AI solutions while remaining hands-on with development.

- Move solutions from discovery and prototype through development, production deployment and production support.

- Lead and guide engineers while contributing to coding, debugging, code reviews, performance optimisation and technical problem solving.

- Work closely with clients and internal stakeholders on discovery, technical workshops, architecture decisions, estimates and solution presentations.

Core Experience & Engineering Requirements :

- 8 - 12+ years of overall software engineering experience.

- 3+ years of strong hands-on experience in AI/ML, GenAI or related AI engineering.

- Strong hands-on Python development.

- Strong recent hands-on experience building GenAI/LLM-based applications.

- Strong experience with LLMs, prompt engineering, structured outputs and tool/function calling.

- Strong coding, debugging, troubleshooting and performance optimisation skills.

- Experience owning solution architecture and technical design for enterprise applications.

- Experience taking solutions from discovery/prototype through development and production deployment.

GenAI, LLM & Agentic AI :

- Hands-on experience with RAG, embeddings, vector databases, document processing, chunking, retrieval and reranking.

- Hands-on experience with AI agents and agent orchestration, including multi-step workflows, tool-using agents, memory/state management and human-in-the-loop patterns.

- Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel or similar frameworks.

- Good understanding of MCP and emerging standards for connecting AI agents with enterprise systems and tools.

- Understanding of secure AI architecture, data protection, prompt/input security, AI guardrails, logging, auditability, monitoring, evaluation, regression testing, scalability and cost management.

Backend, Cloud & Distributed Systems :

- Experience with backend development using FastAPI, Flask, Django or similar frameworks.

- Strong understanding of REST APIs, microservices and distributed application architecture.

- Experience integrating enterprise applications, databases and third-party APIs.

- Hands-on exposure to at least one major cloud platform : Azure, AWS or GCP.

- Experience with Docker, Kubernetes, CI/CD, cloud-native application deployment, API management, logging/monitoring and identity/access management.

- SQL and relational databases; NoSQL databases; vector databases.

- Data ingestion and transformation pipelines; API-based integration; event-driven/asynchronous processing.

- Understanding of enterprise authentication/authorization, data privacy, PII handling and enterprise security requirements.

Technical Leadership & Delivery :

- Experience leading technical teams while continuing to contribute to development.

- Ability to move from Client Problem - Solution Architecture - Technical Design - Team Guidance - Hands-on Coding - Code Review - Deployment - Production Support.

- Break solutions into technical work packages and guide implementation.

- Support estimation, sprint planning and technical task allocation.

- Track technical progress and address dependencies/blockers.

- Mentor team members and improve technical capabilities.

- Review designs and code before higher environments and ensure technical quality throughout the project.

- Work closely with Project Managers, Business Analysts, Solution Architects, QA and DevOps teams.

- Work effectively in Agile delivery environments.

People & Collaboration Skills :

- Strong stakeholder management skills with the ability to build trust, align diverse teams and communicate clearly across technical and business audiences.

- Strong mentoring, coaching and conflict-resolution skills, with the ability to give constructive feedback and create a collaborative engineering culture.

Client-Facing & Pre-Sales Responsibilities :

- Participate in client discovery and technical workshops.

- Understand client landscape, integrations, data, security and infrastructure constraints.

- Explain architecture and technical decisions to technical and business stakeholders.

- Present solution architecture and technical options during client reviews.

- Support pre-sales with technical solutioning, estimates, architecture and feasibility assessments.

- Handle technical questions and challenges during client discussions.

Key Responsibilities :

- Understand business requirements and translate them into the right technical solution.

- Own overall architecture and technical design of AI, GenAI and agentic AI solutions.

- Define application architecture, AI/LLM components, APIs, integrations, data flows, security and deployment approach.

- Evaluate technology and model options based on business need, cost, performance, security and scalability.

- Create architecture diagrams, technical design documents, API specifications and implementation guidelines.

- Identify technical risks and drive practical solutions.

- Actively contribute to coding throughout the project.

- Build critical modules, prototypes, reusable components and integrations.

- Develop and integrate LLM applications, RAG pipelines, AI agents and APIs.

- Support complex coding, integration and performance issues.

- Conduct code reviews and ensure good engineering practices.

- Improve code quality, performance, security and maintainability.

- Lead and guide AI/ML engineers, backend developers and other technical team members.

- Take AI solutions beyond prototype into production, including security, governance, evaluation, monitoring, scalability, performance and cost management.

Good-to-Have Skills :

- Experience with Azure AI Foundry, AWS Bedrock, Google Vertex AI or similar enterprise AI platforms.

- Experience with OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini or equivalent models.

- Traditional ML/ML engineering knowledge.

- LLM evaluation frameworks; AI guardrails and responsible AI; LLM observability and tracing.

- Model and prompt evaluation.

- Token, latency and cost optimisation.

- Experience building enterprise AI accelerators or reusable AI platforms.

- Experience with multi-agent or agentic AI solutions.

- Experience modernising existing enterprise applications using AI.

- Microsoft Fabric or enterprise data platforms.

- BFSI, financial services or other regulated enterprise environments.

- AI security and responsible AI practices.

- Experience supporting technical proposals, estimations and solution presentations.

- Experience mentoring engineers and building engineering standards or reusable frameworks.

- Git-based development, branching, pull requests and code reviews.

- Experience with API management, secrets/configuration management and production troubleshooting.

Preferred Certifications & Qualifications :

Certifications / Credentials :

- Anthropic Claude certifications, particularly CCAF for architects.

- Microsoft AI-103.

- AWS Certified Generative AI Developer - Professional.

Education / Qualification :

- Bachelor's or Master's degree in Computer Science, Engineering, Information Technology or a related discipline.

- Equivalent strong hands-on engineering experience may also be considered.