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

Ai Ml Engineer

AI / ML Engineer