AIML Engineer (Generative AI, Data Analytics)

About the role

A hands-on AIML Engineer is sought to design, build and deploy production-ready data analytics, machine learning, generative AI and agentic AI solutions that deliver measurable business outcomes. The role covers the complete AI lifecycle, from data preparation and model development to deployment, evaluation, monitoring and continuous improvement, and requires strong Python engineering skills.

Key responsibilities

• Translate business challenges into practical analytics, AI and machine learning use cases with measurable success criteria.
• Analyse structured and unstructured data, engineer features and support model development and business decisions.
• Design, build and deploy machine learning and deep learning models in Python with modern AI frameworks.
• Develop generative AI applications using OpenAI, Azure OpenAI, Gemini, Claude or equivalent enterprise LLM platforms.
• Build RAG pipelines, semantic search, embedding workflows and vector-based retrieval systems.
• Design agentic and multi-agent workflows with LangChain, LangGraph, LlamaIndex, CrewAI or similar.
• Develop scalable data pipelines with SQL, Spark, PySpark, Kafka, Airflow and modern data platforms.
• Set up evaluation frameworks for accuracy, relevance, safety, reliability, latency and cost.
• Deploy and manage AI solutions on cloud platforms using Docker, CI/CD and MLOps or LLMOps practices.
• Monitor model performance, data drift, failures, security risks and production quality, applying responsible AI practices.
• Collaborate with product, data, engineering, security and business teams.

Required profile

• 4 to 10 years of experience in AI, machine learning, data analytics, data science or data engineering.
• Production-ready AI solutions built with Python.
• Knowledge of EDA, statistical modelling, feature engineering, ML, DL and model evaluation.
• Experience with LLMs, prompt engineering, RAG, embeddings, semantic search and vector databases.
• Experience with agentic AI frameworks and multi-agent workflows.
• SQL proficiency and scalable data-processing pipeline experience.
• Cloud experience with AWS, Azure or Google Cloud.
• Knowledge of Docker, MLflow, SageMaker, CI/CD or comparable MLOps tools.

Preferred qualifications

• Enterprise production-grade AI and ML solutions.
• NLP, document intelligence, OCR, graph analytics, computer vision or time-series modelling.
• Model fine-tuning, AI observability, guardrails and cost-performance optimisation.
• Snowflake, PostgreSQL, Redshift, Databricks or similar data platforms.
• Regulated industries such as financial services, banking, insurance or healthcare.