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.
Experience Required: 10+ Years
Employment Type: Full Time
Preferred Skills: AI/ML, GenAI, MLOps, Observability, Azure, Conversation AL, Voicebot, Chatbot.
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.