AI/ML Engineer

We are seeking a AI/ML Engineer with a strong technical foundation and proven technical leadership experience to drive the development and deployment of ML and GenAI-powered solutions in a cloud-native environment.

In this role, you will work with a high-impact team of AI/ML, Data and MLOps engineers in a matrixed org structure, collaborate cross-functionally with engineering, product, and DevOps, and own strategic initiatives focused on intelligent workload management, cloud cost optimization, and infrastructure automation.

If you are passionate about building scalable ML systems, driving team performance, and bringing cutting-edge AI (including LLMs and Agentic AI) into production, this is your opportunity to make a meaningful impact.

Key Responsibilities:

- Build end-to-end AI/ML/Agentic-AI solution — from ideation, research, and experimentation to deployment and monitoring in production. - Manage project timelines, deliverables, and cross-team dependencies in coordination with product and engineering leads. - Translate business and technical requirements into ML roadmap, architecture, and actionable workstreams. - Drive adoption of MLOps best practices and champion operational excellence for ML infrastructure. - Build and deploy models for cloud workload prediction, resource optimization, and intelligent automation. - Integrate GenAI, LLM-based and Agentic AI solutions into customer-facing products and internal systems. - Communicate ML strategy, progress, and insights effectively to both technical and non-technical stakeholders.

Requirements:

- BTech/BE, master's or Ph.D. in Computer Science, Machine Learning, AI, or a related field. - 5-8 years of experience in ML/AI/DE - Proven track record of delivering ML projects into production at scale, preferably in a SaaS/cloud infrastructure setting. - Strong hands-on experience with ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch) and Python. - Hands-on experience with GenAI, LLMs and Agentic AI frameworks (e.g., OpenAI, Hugging Face, LangChain), including practical implementation in production.