Senior AI/ML Engineer

NationsBenefits India; Hyderabad; On-site; INR 6,000,000 - 9,000,000; Full time

Job summary

NationsBenefits India is seeking a highly experienced Senior AI/ML Engineer to design, deploy, and monitor enterprise-scale AI/ML systems across the full lifecycle. You will lead end-to-end ML pipelines, optimize inference architectures, and implement observability and governance for production deployments.

The role requires 10+ years of hands-on experience with Python, ML frameworks, vector databases, and MLOps tools, with strong collaboration across business teams to deliver AI-driven

Qualifications

• 10+ years of experience in AI/ML engineering and related domains.
• Experience across end-to-end ML lifecycle from data ingestion to retraining.
• Hands-on Python, SQL, APIs, and major ML frameworks.
• Proficient with vector databases, RAG, embeddings, and LLM orchestration.
• Strong MLOps skills including MLflow, DVC, Docker, Kubernetes, and CI/CD.
• Experience with observability, monitoring, and production governance.

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.
• Dive 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.

Skills

AI/ML expertise

Generative AI

MLOps

Observability

Azure

Conversation AI

Voicebot/Chatbot

Python

SQL

APIs

TensorFlow

PyTorch

Hugging Face

LangChain

Vector databases

RAG pipelines

CI/CD

Kubernetes

Model versioning

Education

Tools

MLflow

DVC

Docker

Kubernetes

CI/CD pipelines

Model versioning

Job description

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 AI Services is a plus.