AI Engineer II

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.

Key Responsibilities

• Architect and lead the development of multi-agent AI systems using frameworks such as LangGraph, CrewAI, and AutoGen — enabling autonomous reasoning, tool use, inter-agent coordination, and adaptive decision-making at enterprise scale.
• Design and operationalize multimodal generative AI pipelines that unify text, image, tabular, and graph data using transformer-based architectures (BERT, CLIP, LLaVA, T5, Whisper, GPT-4o, Gemini) for rich, cross-modal intelligence.
• Build production-grade RAG and Graph-RAG systems integrating vector databases (Pinecone, pgvector, OpenSearch) and knowledge graphs (Neo4j, AWS Neptune) for semantic retrieval, entity-aware reasoning, and grounded generation.
• Lead LLM fine-tuning, prompt engineering, and model alignment strategies — including RLHF, PEFT, LoRA, and instruction tuning — to adapt foundation models for specialized enterprise use cases.
• Establish robust LLMOps and MLOps pipelines on Databricks (AWS) using MLflow, feature stores, prompt evaluation frameworks, model lineage tracking, and continuous retraining workflows to ensure reliable AI delivery.
• Develop high-performance Python backend services for LLM inference orchestration, async job handling, streaming responses, and distributed data workflows supporting high-throughput Gen AI operations.
• Engineer state, memory, and context management subsystems that enable agents to reason temporally, maintain session continuity, manage long-context windows, and coordinate across tools and modalities.
• Implement Responsible AI and AI governance practices — including bias detection, hallucination mitigation, explainability dashboards, output safety guardrails, and compliance with data ethics standards — ensuring transparency and fairness of deployed models.
• Apply traditional ML and statistical modeling (regression, clustering, forecasting, ensemble methods) in hybrid architectures alongside LLMs for interpretable, explainability-first decision systems.
• Continuously research, evaluate, and productionize advancements in generative modeling, agentic AI, multimodal transformers, and frontier foundation models — benchmarking against enterprise-scale performance and safety requirements

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.

• Master's or Bachelor's degree in Computer Science, AI/ML, or Engineering, with significant hands-on experience leading and delivering complex Gen AI or ML engineering programs in production environments.
• Expert-level, hands-on experience designing, building, and deploying large language model (LLM) applications, agentic systems, and RAG pipelines — from prototype to production.
• Deep proficiency with LLM ecosystems: OpenAI, Anthropic, Gemini, Hugging Face, LangChain/LangGraph, and open-source foundation models (LLaMA, Mistral, Falcon, etc.).
• Strong command of Gen AI engineering patterns: prompt engineering, chain-of-thought reasoning, tool/function calling, vector embeddings, semantic search, and agent memory architectures.
• Solid applied knowledge of ML fundamentals — predictive modeling, deep learning (PyTorch, TensorFlow), and statistical techniques — used in tandem with Gen AI for hybrid, interpretable systems.
• Excellent Python engineering skills including async programming, API development (FastAPI), and building inference-ready microservices; SQL proficiency required.
• Hands-on experience with cloud AI infrastructure (AWS SageMaker, Bedrock, Azure OpenAI, or GCP Vertex AI) and familiarity with MLOps/LLMOps tooling (MLflow, Weights & Biases, etc.).
• Strong analytical, communication, and stakeholder management skills — with the ability to translate complex Gen AI concepts into business value and lead cross-functional teams toward delivery.