Sr ML Engineer

Senior Machine Learning Engineer: Location: Bengaluru (HBR Layout, Kalyan Nagar) / Pune | Mode: 5 Days Work from Office | Type: Full-time About Ankercloud: Ankercloud is a global technology consulting and implementation partner that helps ambitious companies turn bold ideas into real products using cloud, data, AI/ML, and security. Ankercloud is a Premier Tier Partner for both AWS and Google Cloud, with teams serving customers across regions and industries. In AI/ML, Ankercloud positions its work around production-grade machine learning, predictive analytics, computer vision, NLP, MLOps, Generative AI, and Agentic AI, with delivery patterns spanning discovery, MVPs, proof-of-value programs, and enterprise-scale rollouts. Role Overview: Ankercloud is hiring a Senior Machine Learning Engineer to design, build, and productionize AI systems that solve high-value customer problems across cloud-native environments. This role is ideal for someone who can move fluidly from problem framing and experimentation to deployment, observability, optimization, and continuous improvement in production. The role sits at the intersection of machine learning engineering, applied research, MLOps, and customer delivery. It requires strong technical depth, good product judgment, and the ability to translate ambiguous business problems into reliable, scalable, and measurable AI solutions for global customers.

• Own the design and development of ML and GenAI solutions from discovery to production, including data preparation, feature engineering, model selection, evaluation, deployment, and iteration.
• Build solutions across domains such as NLP, OCR, computer vision, forecasting, recommendation systems, anomaly detection, synthetic data generation, and intelligent automation.
• Develop enterprise-ready applications using modern LLM and GenAI patterns including prompt engineering, retrieval-augmented generation, embeddings, vector search, tool use, and agentic workflows. Productionize and Scale
• Design, deploy, and maintain robust MLOps pipelines that support repeatable experimentation, CI/CD, model versioning, monitoring, and governance across AWS and GCP environments.
• Use cloud-native AI platforms such as Amazon SageMaker, AWS Bedrock, Vertex AI, and related services to train, tune, deploy, and optimize solutions for performance, reliability, and cost.