Lead AI Engineer

Role Overview
We are looking for a hands-on Lead Engineer - AI to design, build, and deliver scalable AI-powered applications and platforms. The role requires strong engineering leadership, practical experience with LLMs, RAG, agents, prompt engineering, model integration, MLOps, APIs, and cloud-native deployment, along with the ability to mentor engineers and accelerate delivery using AI-assisted development tools.
Key Responsibilities
Lead the design and development of AI/GenAI applications, LLM-powered workflows, agents, copilots, and intelligent automation solutions.
Build production-grade solutions using Python, APIs, LLMs, vector databases, RAG pipelines, embeddings, and orchestration frameworks.
Design and implement Retrieval-Augmented Generation, prompt engineering, function calling, tool use, context management, and evaluation workflows.
Integrate AI capabilities with enterprise applications, APIs, databases, event-driven platforms, and business workflows.
Define architecture patterns for AI solutions, including model selection, data flow, retrieval strategy, guardrails, observability, and cost optimization.
Remain hands-on with coding, prototyping, debugging, code reviews, performance tuning, and production issue resolution.
Establish engineering standards for AI solution development, including testing, evaluation, monitoring, security, privacy, and responsible AI controls.
Mentor engineers and collaborate with architects, data scientists, ML engineers, product teams, security, DevOps, and business stakeholders.
Use approved AI tools such as GitHub Copilot, Cursor, Microsoft Copilot, or equivalent to accelerate coding, testing, documentation, and solution design.
Review and validate AI-generated code and model outputs to ensure correctness, explainability, security, and business alignment.
Required Skills And Experience
8+ years of software engineering experience, including experience in a technical leadership role.
Strong hands-on experience with Python and backend/API development.
Practical experience building AI/GenAI solutions using LLMs, RAG, embeddings, vector databases, prompt engineering, and agents.
Experience with frameworks and platforms such as LangChain, LlamaIndex, Semantic Kernel, Azure OpenAI, OpenAI APIs, Hugging Face, or similar.
Strong understanding of AI application architecture, model integration, data pipelines, and production deployment.
Experience with vector databases such as Azure AI Search, Pinecone, Weaviate, Milvus, Chroma, or FAISS.
Good understanding of MLOps/LLMOps concepts including model evaluation, versioning, monitoring, drift detection, feedback loops, and automated testing.
Strong knowledge of REST APIs, microservices, cloud platforms, CI/CD, Docker, Kubernetes/OpenShift, and observability.
Understanding of AI security, privacy, hallucination mitigation, prompt injection risks, access controls, and responsible AI principles.
Ability to translate business problems into AI use cases with measurable outcomes.
Strong problem-solving, stakeholder management, mentoring, and communication skills.
Preferred Skills
Experience with Azure AI Foundry, Azure OpenAI, Microsoft.Extensions.AI, Semantic Kernel, or MLflow.
Experience designing enterprise copilots, autonomous agents, AI assistants, or workflow automation platforms.
Knowledge of traditional ML, NLP, deep learning, feature engineering, and model-serving patterns.
Experience with Kafka, event-driven architecture, data lakes, data warehouses, or real-time analytics platforms.
Familiarity with AI governance, model risk management, auditability, and compliance requirements.
Experience defining AI evaluation metrics such as accuracy, groundedness, relevance, toxicity, latency, cost, and user feedback.
Exposure to frontend or full-stack development for building AI-enabled user experiences.
Success Measures
Delivery of production-ready AI solutions with measurable business impact.
Improved development speed and quality through responsible use of AI-assisted engineering.
Reliable AI outputs through strong evaluation, monitoring, and guardrail implementation.
Reduction in manual effort through automation, copilots, and intelligent workflows.
Secure and compliant AI solution delivery aligned with enterprise architecture standards.
Improved team capability through mentoring, reusable patterns, and technical leadership.

Work arrangement
No