We are looking for an AI Engineer / Data Scientist with 3–5 years of hands-on experience across classical machine learning, data science and applied Generative AI.
In this role, you will design, build and deploy AI solutions that solve real business problems. You will work across traditional ML models, data analysis, NLP, GenAI, embeddings, vector search, RAG pipelines and LLM-based applications. You will collaborate with business stakeholders, architects, software engineers and data teams to move AI use cases from concept through to production.
You should be comfortable working with Python, SQL, machine learning libraries, cloud platforms and modern GenAI tools. You should also be able to explain your models, evaluate their performance, and understand the risks of using AI in enterprise environments.
Responsibilities include:
Developing machine learning and GenAI solutions for business use cases.
Building models for prediction, classification, recommendation, forecasting, anomaly detection and NLP.
Designing RAG pipelines using embeddings, vector databases and LLMs.
Working with structured and unstructured data.
Building data and ML pipelines for training, evaluation, deployment and monitoring.
Integrating AI models into APIs, applications and enterprise workflows.
Evaluating model quality, reliability, cost, latency and business impact.
Applying responsible AI practices including privacy, security, explainability and human review.
Collaborating with business, engineering and architecture teams.
Required skills:
3–5 years’ experience in data science, machine learning or AI engineering.
Strong Python and SQL skills.
Experience with scikit-learn, XGBoost, PyTorch, TensorFlow or similar.
Experience with NLP, text analytics or document processing.
Hands-on exposure to LLMs, prompt engineering, embeddings, vector search and RAG.
Experience with cloud platforms such as AWS, Azure or Google Cloud.
Understanding of MLOps, CI/CD, model deployment and monitoring.
Strong communication and problem-solving skills.
Nice to have:
Experience with AWS Bedrock, SageMaker, Azure OpenAI, Databricks or similar.
Experience with LangChain, LangGraph, LlamaIndex, AWS Strands, MCP, Agent frameworks.
Experience with vector databases such as Pinecone, FAISS, OpenSearch, Chroma or pgvector.
Experience in financial services, SaaS, consulting, enterprise platforms or regulated environments.
Understanding of responsible AI, privacy, security and model governance.