Mid to Senior AI Engineer – Generative AI & MLOps

About the role

We are seeking a Mid to Senior AI Engineer to design, build, and deploy production‑grade AI solutions across cloud and on‑premises environments. The role blends Generative AI, Machine Learning, MLOps, and enterprise software development, working directly with clients to transform business challenges into practical AI applications.

Key responsibilities

• Design and develop Generative AI applications such as Retrieval‑Augmented Generation, LLM‑powered assistants, and autonomous agents using API‑based and self‑hosted models.
• Fine‑tune and optimize models with prompt engineering, embeddings, instruction tuning, LoRA, and PEFT techniques.
• Build classical ML models for forecasting, recommendation, anomaly detection, classification, and predictive analytics.
• Productionise AI models as scalable APIs and enterprise services on Azure, AWS, OCI, GCP, and on‑premises platforms.
• Implement MLOps pipelines covering CI/CD, model versioning, drift monitoring, automated retraining, and observability.
• Optimize inference through quantization, batching, caching, GPU optimization and efficient resource utilization.

Required profile

• Mid‑level to senior professional with hands‑on experience in AI engineering and MLOps.
• Proven ability to deliver secure, scalable AI solutions for regulated, data‑sensitive industries.
• Experience working in both cloud (Azure, AWS, OCI, GCP) and air‑gapped on‑premises environments.
• Strong problem‑solving skills and ability to engage directly with clients.

Required skills

• Python
• Generative AI
• Retrieval‑Augmented Generation (RAG)
• 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 and secure, air‑gapped environments
• MLOps, CI/CD, model versioning, drift detection, automated retraining, observability
• Quantization, batching, caching, GPU optimization, resource utilization
• API integration and self‑hosted open‑source model deployment