Senior Lead Engineer - AI

Job Requirements

At Quest Global, it’s not just what we do but how and why we do it that makes us different. With over 25 years as an engineering services provider, we believe in the power of doing things differently to make the impossible possible. Our people are driven by the desire to make the world a better place—to make a positive difference that contributes to a brighter future. We bring together technologies and industries, alongside the contributions of diverse individuals who are empowered by an intentional workplace culture, to solve problems better and faster.

We are seeking a Senior Lead AI Engineer, to drive the end-to-end delivery of complex AI project. The ideal candidate will lead technical design and implementation, collaborate with cross-functional stakeholders, conduct architecture and code reviews, provide technical leadership to engineering teams, and ensure scalable, high-quality AI solutions are delivered successfully.

Key Responsibilities
Lead the technical design and implementation of enterprise-scale data and AI solutions, ensuring alignment with business objectives and technology standards

- Review solution designs, code, and technical deliverables to ensure quality, performance, and adherence to architectural standards
- Mentor and guide development teams by resolving technical challenges and ensuring best practices
- Design and implement production-ready applications using LLMs (GPT-4, Claude, Gemini) and other foundation models.
- Build and optimize RAG (Retrieval-Augmented Generation) systems using vector databases like Pinecone, Weaviate, or Qdrant.
- Design and implement hybrid RAG systems, specifically utilizing LightRAG / GraphRAG (Knowledge Graphs) alongside vector databases to enable multi-hop reasoning across complex operational data.
- Implement model fine-tuning pipelines for domain-specific applications using techniques like LoRA and QLoRA.
- Create multi-agent systems and complex AI workflows using frameworks like LangGraph or AWS Strands, including Amazon Bedrock for foundation models.
- Integrate multiple AI models (text, vision, audio) to create multimodal applications and work with protocols like MCP and A2A to extend the capabilities of the system.
- Architect secure, event-driven integrations between the AI platform and Enterprise ITSM tools (e.g., Jira, MS Teams, GitLab) using webhooks and message brokers.
- Implement guardrails and safety measures to ensure responsible AI deployment.
- Model Development: Design and implement AI/ML models, algorithms, and pipelines.
- Collaboration: Work with data scientists, engineers, and business stakeholders to deliver AI-driven solutions.
- Optimization & Evaluation: Continuously monitor and improve AI systems for accuracy and efficiency.
- Compliance & Ethics: Ensure AI solutions adhere to ethical standards and regulatory requirements (GDPR, fairness, bias mitigation).
- Leadership: Guide cross-functional teams and mentor junior engineers in AI best practices
- Security & Compliance: Data anonymization , secure model deployment , bias detection

We are known for our extraordinary people who make the impossible possible every day. Questians are driven by hunger, humility, and aspiration. We believe that our company culture is the key to our ability to make a true difference in every industry we reach. Our teams regularly invest time and dedicated effort into internal culture work, ensuring that all voices are heard.

We wholeheartedly believe in the diversity of thought that comes with fostering a culture rooted in respect, where everyone belongs, is valued, and feels inspired to share their ideas. We know embracing our unique differences makes us better, and that solving the worlds hardest engineering problems requires diverse ideas, perspectives, and backgrounds. We shine the brightest when we tap into the many dimensions that thrive across over 21,000 difference-makers in our workplace.

Work Experience

- Programming Languages: 5+ years of Python development experience with strong software engineering fundamentals.
- Bachelor's or Master's degree in Computer Science, AI/ML, or equivalent practical experience
- Hands-on experience building applications with LLM APIs (OpenAI, Anthropic, Google, etc.)
- Machine Learning & Deep Learning: Expertise in TensorFlow , PyTorch , Hugging Face ; model selection, evaluation, and interpretability.
- Strong knowledge of prompt engineering techniques and in-context learning.
- Experience with vector databases and embedding models for semantic search.
- Experience with Graph databases (e.g., Neo4j ) and implementing GraphRAG / LightRAG architectures (e.g., LightRAG ).
- Experience with AI memory management frameworks (e.g., Mem0 ) to maintain stateful, multi-turn conversational context and episodic memory .
- Familiarity with cloud platforms ( AWS , GCP, Azure) and containerization (Docker, Kubernetes ).
- Deep hands-on experience with AWS , specifically Amazon EKS , Istio / Kubernetes Gateway API , and managing real-time WebSocket connections .
- Excellent problem-solving skills and ability to work with ambiguous requirements.
- Strong communication skills to explain complex AI concepts to various stakeholders.
- Security & Compliance: Data anonymization , secure model deployment , bias detection