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