Senior AI/ML Engineer

Experience: 10+ Years

Location: Hyderabad, India

Employment Type: Full-time

Role Summary:
We are looking for a highly experienced Senior AI/ML Engineer with strong hands-on expertise in designing,
developing, deploying, and monitoring enterprise-scale AI/ML systems. The candidate must possess end-to-end
experience across the complete AI/ML lifecycle, including model development, deployment, observability,
governance, optimization, and production support.

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.
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

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