Tech Lead

• 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

• 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

• 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

• 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

• 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

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

• Experience leading technical teams while continuing to contribute to development

• Strong client-facing and communication skills

• Experience working in Agile delivery environments

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

• 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

• 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

• 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

• Ensure technical quality throughout the project

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

• Own technical delivery and ensure alignment with agreed architecture

• 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

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

• 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