AI/ML Engineer - LLM & MLOps

Job Summary:

We are looking for an experienced AI/ML Engineer with hands-on experience in machine learning, deep learning, and LLM-based application development. The candidate will design, build, and deploy scalable AI systems with a strong focus on MLOps, LLMOps, cloud platforms, and AI governance.

Key Responsibilities:

Machine Learning & AI Development:

- Design, develop, and optimize machine learning and deep learning models using PyTorch, TensorFlow, or JAX.

- Build and deploy LLM-based applications using frameworks such as LangChain, LangGraph, and LlamaIndex.

- Develop NLP pipelines for text processing, classification, and generative AI use cases.

- Implement computer vision models using OpenCV when required.

- Apply prompt engineering and Retrieval-Augmented Generation (RAG) techniques.

MLOps, Cloud & Deployment:

- Build and manage end-to-end ML pipelines using MLflow and Kubeflow.

- Deploy, monitor, and maintain models on Azure ML and cloud-native environments.

- Containerize applications using Docker and orchestrate using Kubernetes.

- Implement CI/CD pipelines for ML workflows.

- Use Infrastructure as Code (IaC) tools such as Terraform and CloudFormation.

Experimentation & Monitoring:

- Design and execute A/B testing and hypothesis testing frameworks.

- Monitor model performance, drift, and production metrics.

- Implement logging, monitoring, and alerting systems.

Governance, Security & Compliance:

- Implement AI governance frameworks such as NIST AI RMF and EU AI Act.

- Ensure compliance with data privacy regulations (HIPAA, GDPR).

- Implement Role-Based Access Control (RBAC), audit logging, and data privacy controls.

- Follow secure Software Development Life Cycle (SDLC) practices.

Required Skills:

Core AI/ML Skills:

- Strong experience in machine learning and deep learning.

- Proficiency in PyTorch, TensorFlow, or JAX.

- Experience with Scikit-learn and XGBoost.

- Solid understanding of Natural Language Processing (NLP).

LLM & Generative AI:

- Hands-on experience with LangChain, LangGraph, or LlamaIndex.

- Experience with Hugging Face Transformers.

- Strong understanding of prompt engineering and RAG architectures.

Data Engineering & Databases:

- Experience with SQL databases (MySQL, PostgreSQL).

- Experience with NoSQL databases (MongoDB).

- Knowledge of vector databases such as Weaviate or ChromaDB.

- Familiarity with graph databases like Neo4j.

MLOps & DevOps:

- Experience with MLflow and Kubeflow.

- Strong knowledge of Docker and Kubernetes.

- Experience with CI/CD pipelines.

- Familiarity with Terraform and CloudFormation.

Cloud Platforms:

- Hands-on experience with Azure ML.

- Experience with Amazon Bedrock.

- Familiarity with Semantic Kernel.

- Experience with IBM Watson is a plus.

Governance & Security:

- Understanding of AI governance frameworks.

- Knowledge of data privacy regulations (HIPAA, GDPR).

- Experience building secure and compliant ML systems.

Preferred Qualifications (Nice to Have):

- Experience with multi-agent systems.

- Knowledge of distributed training and large-scale optimization.

- Experience with real-time ML systems.

- Experience in regulated industries such as healthcare or finance.