Artificial Intelligence Engineer

We are looking for a Machine Learning & Generative AI Engineer with strong hands-on expertise in Python, Machine Learning, Deep Learning, NLP, and Generative AI. The ideal candidate will be responsible for developing, evaluating, and deploying intelligent ML/AI solutions, with a strong focus on LLM applications, RAG, prompt engineering, embeddings, and agentic workflows.

The role requires a combination of strong statistical and machine learning fundamentals along with practical experience building production-ready Generative AI applications.

Key Responsibilities

Design, develop, train, and evaluate machine learning and deep learning models using Python.

Apply techniques in NLP, statistical modelling, experimentation, feature engineering, and model evaluation.

Build and optimize ML pipelines for real-world business use cases.

Develop Generative AI and LLM-powered applications.

Design and implement Retrieval-Augmented Generation (RAG) pipelines using embeddings and vector databases.

Develop effective prompt engineering strategies for LLM-based applications.

Work with LLM application frameworks such as LangChain, LlamaIndex, or equivalent.

Build and evaluate agentic workflows and AI agents for complex tasks.

Implement guardrails, safety mechanisms, and evaluation frameworks for LLM applications.

Work with vector databases for efficient storage and retrieval of embeddings.

Conduct experimentation and A/B testing to assess model and application performance.

Monitor, evaluate, and continuously improve model accuracy, reliability, latency, and scalability.

Collaborate with product, engineering, and data teams to translate business problems into AI/ML solutions.

Stay updated with emerging developments in Generative AI, LLMs, NLP, and AI engineering.

Required Skills

Machine Learning & Data Science

Strong proficiency in Python.

Hands-on experience with Machine Learning and Deep Learning.

Strong understanding of NLP concepts and techniques.

Experience with statistical modelling and experimentation.

Strong knowledge of feature engineering and model evaluation.

Proficiency in SQL.

Experience with scikit-learn.

Hands-on experience with PyTorch or TensorFlow.

Generative AI / LLM

Hands-on experience with Generative AI and LLM application development.

Strong expertise in Prompt Engineering.

Experience building RAG-based applications.

Understanding of embeddings and vector databases.

Experience with LLM evaluation frameworks and evaluation methodologies.

Experience implementing guardrails for LLM applications.

Understanding of agentic AI workflows / AI agents.

Experience with LangChain, LlamaIndex, or equivalent LLM application frameworks.

Good to Have

Experience working with multiple LLM providers/models.

Knowledge of LLM fine-tuning, RAG optimization, or model adaptation techniques.

Experience with cloud-based AI/ML platforms.

Understanding of MLOps and model deployment practices.

Experience taking AI/ML prototypes into production.

Knowledge of observability, monitoring, and performance optimization for LLM applications.

Ideal Candidate

3+ years of relevant experience in Machine Learning, Data Science, AI Engineering, or a related field.

Strong programming and problem-solving capabilities.

Strong combination of traditional ML + Generative AI/LLM expertise.

Hands-on approach with the ability to build solutions from experimentation through production.

Comfortable working in a remote and collaborative environment.

Strong analytical mindset with a focus on experimentation, evaluation, and continuous improvement.