Senior AI/ML Engineer

Role Overview :

We are looking for an experienced Senior AI/ML Engineer with strong hands-on expertise in Generative AI, Agentic AI, LLMs, RAG, AI evaluation, cloud-native engineering, and solution architecture. The role will involve designing, developing, evaluating, and deploying production-grade AI solutions while working closely with business and technical stakeholders to translate complex and ambiguous requirements into scalable, secure, and reliable solutions.

Key Responsibilities :

• Design, develop, and deploy production-grade AI/ML and Generative AI solutions for complex business use cases.

• Build and integrate LLM-powered applications, AI agents, copilots, intelligent automation, and agentic workflows.

• Design and implement scalable RAG pipelines, including data ingestion, chunking, embeddings, retrieval, context management, and response generation.

• Develop and optimize prompt engineering, orchestration, tool/function calling, and agent workflows.

• Design and implement AI evaluation frameworks, including evaluation datasets, scoring methodologies, rubrics, agent-performance testing, and failure-mode analysis.

• Establish mechanisms to measure and improve accuracy, relevance, reliability, safety, latency, and overall AI solution performance.

• Design scalable, resilient, and secure cloud-native AI architectures using modern distributed systems and microservices principles.

• Develop and manage Infrastructure as Code (IaC) for AI solutions and cloud environments, particularly on Microsoft Azure.

• Build and maintain APIs, services, data integrations, and supporting components required for enterprise AI applications.

• Translate business and technical requirements into solution architectures, system designs, technical specifications, and implementation plans.

• Lead technical discovery sessions with clients and stakeholders to understand business problems, identify AI opportunities, and define practical solution approaches.

• Take AI solutions from prototype/PoC through production deployment, monitoring, optimization, and support.

• Collaborate with Data Engineering, Product, Architecture, Cloud, Security, and business teams to deliver end-to-end solutions.

• Conduct technical design reviews and code reviews and promote reusable engineering patterns and best practices.

• Troubleshoot complex issues across AI applications, APIs, data pipelines, cloud infrastructure, and distributed systems.

• Optimize AI workloads for scalability, performance, reliability, latency, and cost.