Senior AI/ML Engineer

Key Responsibilities

• Lead development and deployment of enterprise-scale AI/ML and Generative AI solutions.

• Build and optimize end-to-end ML pipelines including experimentation, deployment, monitoring, and retraining.

• Develop scalable inference architectures, vector search systems, and RAG-based applications.

• Implement observability, monitoring, governance, and production support mechanisms for AI systems.

• Drive model lifecycle management including versioning, experimentation tracking, and CI/CD automation.

• Mentor engineering teams and establish AI/ML engineering best practices.

• Collaborate with business stakeholders to identify and implement AI-driven solutions.

• Optimize AI systems for scalability, latency, reliability, and cost efficiency.

Required Skills & Qualifications

• Strong hands-on experience in Artificial Intelligence, Machine Learning, and Generative AI systems.

• Extensive experience in end-to-end ML lifecycle including data ingestion, feature engineering, model development, validation, deployment, monitoring, and retraining.

• Hands-on expertise with Python, SQL, APIs, and ML frameworks such as Scikit-learn, TensorFlow, PyTorch, Hugging Face, and LangChain.

• Experience with Vector Databases, RAG pipelines, semantic search, embeddings, and LLM orchestration.

• Strong expertise in MLOps tools including MLflow, DVC, Docker, Kubernetes, CI/CD pipelines, and model versioning.

• Hands-on experience with observability, logging, tracing, monitoring, drift detection, and model performance tracking.

• Experience building scalable cloud-native inference and AI deployment pipelines.

• Strong understanding of distributed systems, data engineering, and scalable AI infrastructure.

• Excellent stakeholder management and cross-functional collaboration skills.

• Experience with Azure Cloud Stack is a plus.