AI / ML Engineer

Job Responsibilities

• Design, train, and evaluate ML models for real client problems — classification, regression, clustering, and beyond
• Design, build, and optimize databases — write complex, efficient SQL queries across any RDBMS (PostgreSQL, MySQL, SQL Server, Oracle, etc.)
• Build and manage data warehouses and vector databases; model data for analytics, retrieval, and ML workloads
• Build RAG systems and AI agents — retrieval pipelines, vector databases, embeddings, and agentic workflows
• Develop agentic AI using frameworks like Lang Chain, Lang Graph, or whichever AI tech suite best fits the task
• Integrate LLM APIs and embeddings into production features — chatbots, assistants, and decision-intelligence tools
• Build and ship full-stack applications end-to-end using AI-assisted development, ready to work in any technology stack the project needs
• Handle the full data workflow — ingestion, preprocessing, feature engineering, querying, and analysis
• Turn raw data into clear insights — strong analytics to spot patterns, validate hypotheses, and guide decisions
• Work in an agile model-development cycle — rapid iteration, experimentation, and continuous improvement
• Use Git / GitHub rigorously for version control, collaboration, and code review
• Collaborate with the team to turn client requirements into deployed, working AI solutions

Core Technical Skills

• Strong database fundamentals — hands-on with at least one RDBMS (PostgreSQL, MySQL, SQL Server, or Oracle) and able to write complex, optimised SQL queries (joins, subqueries, window functions, aggregations)
• Solid data modelling and schema design — normalization, indexing, and query performance tuning
• Data warehouse experience is strongly preferred — Big Query, Redshift, Snowflake, or similar
• Python (primary) plus strong SQL; Java or Scala is a plus
• ML libraries: scikit-learn, TensorFlow, PyTorch, Keras
• Data handling: Pandas, NumPy, data preprocessing, feature engineering
• ML fundamentals — supervised / unsupervised learning, regression, classification, clustering, and model-evaluation metrics
• Deep learning basics (CNNs, RNNs) and GenAI / LLMs — prompt engineering, RAG, LangChain / LangGraph, vector databases
• Vector databases & embeddings — hands-on with retrieval and semantic search
• AI-assisted full-stack development (vibe coding) — ready to build in any stack using tools like Claude, Cursor, or Copilot
• Version control: strong Git / GitHub workflow expertise