JD - AI/ML Engineer - Advanced Data Scientist

Responsibilities:

- Design, develop, and deploy ML and deep learning models for enterprise AI applications (XGBoost, TensorFlow, PyTorch)
- Build and maintain GenAI pipelines including RAG architectures using LLMs and vector databases
- Develop knowledge graph-based RAG systems using graph databases, enabling hybrid retrieval strategies
- Perform feature engineering, model lifecycle management, and performance evaluation across ML workflows
- Integrate LLMs and GenAI components (LangChain, OpenAI, HuggingFace) into production-grade backend systems
- Collaborate with AI engineers, data engineers, and product managers to translate research prototypes into scalable solutions
- Ensure robustness, accuracy, and explainability of AI/ML models in production environments
- Contribute to architecture discussions, model design reviews, and retrieval performance tuning
- Mentor junior team members and contribute to best practices in applied ML and GenAI engineering

Educational & Experience Requirements:

- Bachelors degree in computer science, Data Science, Artificial Intelligence, or a related field
- 2-4 years of hands-on industry experience in ML, DL, or GenAI engineering




- Equivalent practical experience and demonstrated project work will be considered in lieu of formal qualifications

Technical Requirements:

- Strong proficiency in Python, machine learning (XGBoost), and deep learning frameworks (TensorFlow, PyTorch)
- Solid experience with NLP, LLMs, and transformer-based models (GPT, BERT)
- Hands-on experience with the GenAI stack: LangChain, OpenAI APIs, HuggingFace
- Proven experience building RAG pipelines including chunking, embeddings, and retrieval strategies
- Working knowledge of vector databases (FAISS, Pinecone, Chroma)
- Critical differentiator: Experience with knowledge graph-based RAG using Neo4j and Cypher queries, and hybrid RAG architectures combining graph and vector retrieval
- Familiarity with MLOps practices (MLflow, CI/CD, model deployment) is a strong plus
- Exposure to agentic AI or multi-agent systems is an added advantage
- Experience with version control (Git) and Agile development practices

Behavioural Requirements:

- Excellent problem-solving skills and attention to detail
- Robust written and verbal communication skills in English
- Ability to work collaboratively in cross-functional teams
- Passion for pushing the boundaries of applied AI and building systems that deliver measurable enterprise impact

📌 Ai Ml Engineer (Bengaluru)
🏢 BootLabs
📍 Bengaluru