Artificial Intelligence Engineer

AI / Machine Learning Engineer – Generative AI, LLM & RAG

Experience: 3+ Years

Age Criteria: Below 28 Years

Education: B.Tech / M.Tech from Tier-1 Institutes (IITs, NITs, BITS)

CTC Structure: 75% Fixed + 25% Variable

Domain: AI / ML | NLP | Generative AI | Semantic Search

Job Overview

We are looking for a talented AI / Machine Learning Engineer with strong hands-on experience in Artificial Intelligence, Machine Learning, Natural Language Processing (NLP), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG).

The ideal candidate will contribute to building and optimizing enterprise-grade AI-powered search and recommendation systems. You will work on semantic search, intelligent information retrieval, ranking algorithms, personalization, and LLM-powered applications across business domains such as Banking, Loans, Insurance, and Investments.

This role is ideal for engineers passionate about developing scalable, production-ready AI/ML solutions and transforming complex data into intelligent business applications.

Key Responsibilities

• Design, develop, and optimize enterprise-grade AI/ML models and intelligent search solutions.
• Build and enhance semantic search, vector search, and information retrieval systems.
• Develop ML algorithms for search ranking, query relevance, personalization, and recommendation systems.
• Implement NLP and Generative AI solutions using LLMs, embeddings, and transformer-based architectures.
• Design and implement RAG pipelines, including document processing, chunking, indexing, retrieval, and response generation.
• Develop and maintain taxonomy, ontology, metadata models, and knowledge retrieval frameworks.
• Collaborate with business stakeholders across Banking, Loans, Insurance, and Investments to deliver AI-powered solutions.
• Perform data analysis, feature engineering, model evaluation, and performance optimization.
• Build scalable, production-grade ML applications and integrate them with enterprise systems.
• Work closely with engineering, product, and design teams to deliver end-to-end AI solutions.
• Participate in AI/ML proof-of-concept initiatives and evaluate emerging technologies.
• Implement engineering best practices, including Git, CI/CD, automated testing, model monitoring, and deployment.
• Stay updated with advancements in Generative AI, LLMs, NLP, and Machine Learning.

Mandatory Skills & Qualifications

1. Experience

• Minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI.2. Programming & Data Skills
• Strong proficiency in Python and SQL.
• Hands-on experience in data analysis, feature engineering, model development, and production-grade ML applications.3. Machine Learning & Deep Learning
• Experience with frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.4. NLP & Semantic Search
• Hands-on experience in NLP, text classification, embeddings, semantic search, document understanding, or recommendation systems.5. Large Language Models (LLMs)
• Practical experience working with foundation models such as GPT, Llama, Mistral, Claude, Gemini, Phi, or equivalent.6. Retrieval-Augmented Generation (RAG)
• Hands-on experience building or implementing RAG-based applications, vector search, knowledge retrieval, embeddings, document chunking, and indexing.7. Production & Engineering Practices
• Experience with Git, CI/CD pipelines, production environments, and scalable AI/ML applications.8. Educational Qualification
• B.Tech / M.Tech from Tier-1 institutes, including IITs, NITs, and BITS.9. Age Criteria
• Candidate must be below 28 years of age.10. Compensation Structure
• CTC breakup: 75% Fixed + 25% Variable, as per company policy.

Good to Have / Preferred Skills

• MLOps / LLMOps: MLflow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or similar frameworks.
• Vector Databases: Pinecone, Weaviate, Milvus, Chroma, FAISS, Qdrant, or equivalent.
• Big Data Technologies: Apache Spark, PySpark, distributed ML pipelines, and large-scale data processing.
• Cloud & Deployment: Docker, Kubernetes, AWS, Microsoft Azure, GCP, and cloud-native AI deployments.
• AI Architecture: Experience designing scalable ML architectures and real-time AI/ML systems.
• Industry Background: Experience in AI-first startups, FinTech, Banking, Lending, Fraud Analytics, Risk Analytics, SaaS, or product-based technology companies.

Ideal Candidate Profile

We are looking for an engineering professional who combines strong fundamentals in Machine Learning and Deep Learning with practical experience in Generative AI, LLMs, and RAG-based applications.

Candidates should have the ability to develop, deploy, and optimize AI solutions in real-world production environments rather than having experience limited to academic projects or theoretical implementations.