AI Engineer – Generative AI, MLOps & Cloud Solutions

About the role

JBS Alpha, the AI and emerging technology practice of Jaffer Business Systems, is seeking a Mid to Senior AI Engineer to design, build and deploy production‑grade AI solutions. You will work on Generative AI, Machine Learning and MLOps projects for clients in regulated, data‑sensitive industries, delivering secure and scalable systems on cloud or air‑gapped environments.

Key responsibilities

• Design and develop Generative AI applications such as Retrieval‑Augmented Generation, LLM‑powered assistants and autonomous agents.
• Fine‑tune and optimize models using prompt engineering, embeddings, instruction tuning, LoRA and PEFT.
• Build classical ML models for forecasting, recommendation, anomaly detection and predictive analytics.
• Create evaluation frameworks to assess model quality, latency, cost, reliability and safety.
• Productionise models as scalable APIs and enterprise services across Azure, AWS, OCI, GCP and on‑premises platforms.
• Design secure deployments for air‑gapped and regulated environments.
• Develop and maintain MLOps pipelines covering CI/CD, model versioning, monitoring, drift detection, automated retraining and observability.
• Optimize inference through quantization, batching, caching and GPU utilization.
• Collaborate with software engineers to embed AI capabilities into enterprise applications.
• Engage directly with clients to translate business challenges into practical AI solutions.

Required profile

• Mid to senior‑level AI engineer with hands‑on experience delivering end‑to‑end AI projects.
• Comfortable working in both cloud and on‑premises environments, especially in regulated sectors.
• Strong problem‑solving skills and ability to communicate technical concepts to clients.

Required skills

• Generative AI, Retrieval‑Augmented Generation, Large Language Models (LLM)
• Prompt engineering, embeddings, instruction tuning, LoRA, PEFT
• Machine Learning (forecasting, recommendation, anomaly detection, classification, predictive analytics)
• Azure, AWS, OCI, GCP, on‑premises deployment
• MLOps, CI/CD, model versioning, monitoring, drift detection, automated retraining, observability
• Quantization, batching, caching, GPU optimization