Ai Ml Engineer

Role & responsibilities

We are looking for a skilled AI/ML Engineer to design, develop, integrate, and deploy production-ready Artificial Intelligence and Machine Learning solutions.

The ideal candidate should have strong experience in Python, Machine Learning, Deep Learning, Generative AI, Large Language Models (LLMs), RAG, AI Agents, and MLOps.

The role requires more than building experimental models. The candidate should be capable of taking AI solutions through the complete lifecycle, from understanding business requirements and preparing data to development, integration, deployment, monitoring, and continuous improvement.

• Design, develop, and deploy Machine Learning and AI solutions.
• Build and optimize supervised and unsupervised Machine Learning models.
• Develop Deep Learning solutions using PyTorch, TensorFlow, or Keras.
• Design and implement Generative AI and LLM-based applications.
• Build Retrieval-Augmented Generation (RAG) systems.
• Implement semantic search using embeddings and vector databases.
• Develop AI Agents capable of using tools, APIs, databases, and external services.
• Implement function calling, tool calling, and structured AI outputs.
• Design multi-step AI workflows and agent-based automation.
• Integrate AI solutions with existing applications through REST APIs.
• Develop AI services using FastAPI, Flask, or similar frameworks.
• Design and maintain data preprocessing and feature-engineering pipelines.
• Work with structured and unstructured data.
• Build document-processing and knowledge-base solutions.
• Evaluate AI/ML models for accuracy, reliability, performance, and scalability.
• Evaluate LLM outputs and implement techniques to reduce hallucinations.
• Implement AI guardrails and output validation.
• Optimize LLM token usage, latency, and operational cost.
• Deploy and maintain AI/ML services in production environments.
• Implement model and application monitoring, logging, and observability.
• Identify and monitor model drift and data drift.
• Build automated testing and CI/CD pipelines for AI applications.
• Work with cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform.
• Apply appropriate security, privacy, authentication, and authorization practices to AI systems.
• Research and evaluate new AI models, frameworks, and technologies when appropriate.

Preferred candidate profile

Strong proficiency in Python

Clean Code and Software Design Principles

Linux fundamentals

REST APIs

Data Structures and Algorithms

Supervised and Unsupervised Learning

Data Preprocessing

Scikit-learn

XGBoost / LightGBM

SQL

NumPy

Pandas

Data Cleaning and Transformation

ETL / ELT

Relational Databases

NoSQL fundamentals

Data Pipelines

Data Quality and Validation

Experience working with large datasets