AI / ML Engineer

Role & responsibilities

• Design and implement machine learning models for classification, regression, clustering, NLP, or computer vision tasks.
• Build and maintain scalable ML pipelines for training, validation, and deployment.
• Optimize model performance and ensure robustness in production environments.
• Build and fine-tune large language models (LLMs) for tasks such as summarization, Q&A, content generation, and code synthesis.
• Relational AI experience- Design and implement relational knowledge graphs to model complex data relationships.
• Use declarative logic and reasoning frameworks to extract insights from structured data.
• Integrate AI models into production systems using APIs or microservices.
• Work with DevOps/ML-Ops teams to automate model deployment and monitoring.
• Ensure model explainability, fairness, and compliance with ethical AI standards.
• Work cross-functionally with product managers, analysts, and engineers.
• Document model architecture, data pipelines, and performance metrics.
• Participate in code reviews and contribute to best practices in AI/ML strategic development.

Preferred candidate profile:

• Bachelor's or Masters degree in Computer Science, Data Science or AI related field.
• 5+ years of relevant experience in AI/ML engineering roles.
• Hands-on experience with:
• Generative AI models (e.g., GPT, LLaMA, Claude, DALL•E)
• Python, PyTorch/TensorFlow, Hugging Face Transformers
• Relational data modeling and knowledge graph tools (e.g., SQL, RDF, SPARQL, RelationalAI)
• Prototyping tools (Streamlit, Gradio, Flask)
• Cloud platforms (AWS, Azure)
• Strong understanding of NLP, deep learning, and model evaluation techniques.
• Experience with vector databases (e.g., FAISS, Pinecone) and retrieval-augmented generation (RAG).
• Familiarity with LangChain, LlamaIndex, or similar GenAI orchestration frameworks.
• Exposure to CI/CD, Docker, and Kubernetes for ML deployment.
• Strong communication and collaboration skills.