SR AI Engineer

About the Role

We are looking for a SR AI Engineer to design, build, and scale Generative AI solutions that automate processes and power conversational assistants, including Retrieval‑Augmented Generation (RAG) and agent‑based systems.

In this role, you will work end‑to‑end, from discovery and prototyping to production deployment and operation with real users. You will architect cloud‑native solutions on Azure and/or AWS, integrate with enterprise platforms, and balance quality, safety, cost, and performance. This is a hands‑on senior role with the opportunity to lead technical initiatives and shape best practices across projects.

Key Responsibilities

• Engage directly with client stakeholders to identify, refine, and prioritize Generative AI use cases.
• Act as a technical lead, defining architecture, quality standards, and best practices for AI solutions.
• Architect robust GenAI solutions on Azure and/or AWS, including LLM orchestration, RAG, API integrations, security, observability, and cost control.
• Design and deliver conversational assistants and copilots, covering prompt design, tool calling, grounding strategies, guardrails, and memory management.
• Implement and optimize RAG pipelines: ingestion, chunking, embeddings, vector stores, retrieval evaluation, reranking, caching, and latency optimization.
• Evaluate and harden agentic systems using quality, robustness, and groundedness metrics.
• Own LLMOps/MLOps practices in production environments, including containerization, monitoring, and performance tracking.

Required Skills & Qualifications

• Bachelor’s, Master’s, or PhD in Machine Learning, Computer Science, Engineering, Mathematics, or related fields.
• Proven experience in senior AI engineering roles with strong exposure to Generative AI.
• Extensive experience designing and scaling GenAI applications, including RAG architectures and autonomous or agent‑based systems.
• Strong cloud experience on Azure and/or AWS (IAM, networking, compute, storage, monitoring, security, cost management).
• Solid software engineering skills in Python, including API integration, testing, code reviews, and documentation.
• Strong product and business mindset, able to define KPIs and clearly communicate impact to non‑technical stakeholders.

Nice to Have

• Experience leading small technical teams or mentoring other engineers.
• Exposure to deep learning frameworks such as PyTorch or TensorFlow.
• Hands‑on experience with LLMOps/MLOps in production environments.
• Azure or AWS AI/ML certifications.
• Background in IT consulting or client‑facing environments.