Ai Ml Engineer

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

• Strong 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