Artificial Intelligence Engineer

The Reliable Jobs; India; On-site; INR 2,800,000 - 4,400,000; Full time

Job summary

The Reliable Jobs is seeking a Machine Learning & Generative AI Engineer with strong hands‑on Python, ML/DL, NLP, and Generative AI skills. You will build, evaluate, and deploy production‑grade AI solutions, with a focus on LLMs, RAG, embeddings, and prompt engineering.

Responsibilities include designing ML pipelines, developing RAG architectures using vector stores, integrating LangChain or LlamaIndex, and collaborating with product and data teams to turn business problems into scalable AI

Qualifications

• 3+ years of hands-on ML/AI experience in production settings.
• Strong Python proficiency with ML/DL fundamentals.
• Solid background in NLP, embeddings, and evaluation metrics.
• Experience deploying GenAI/LLM solutions and RAG architectures.

Responsibilities

• Design, train, and evaluate ML/DL models in Python.
• Develop NLP and generative AI solutions for real‑world use cases.
• Build ML pipelines and production‑ready workflows.
• Implement RAG pipelines using embeddings and vector stores.
• Design prompts and guardrails for LLM applications.
• Work with LangChain, LlamaIndex, or equivalents.
• Collaborate with product, engineering, and data teams to translate business problems into AI solutions.
• Stay current with Generative AI, LLMs, and NLP trends.

Skills

Python

Machine Learning

Deep Learning

NLP

Generative AI

Prompt Engineering

LLM Applications

RAG

Embeddings

Vector Databases

LangChain

LlamaIndex

SQL

PyTorch

TensorFlow

Tools

LangChain

LlamaIndex

Vector Stores

PyTorch

TensorFlow

Job description

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

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.