AI Engineer

We are looking for an AI Engineer who can work across the AI engineering lifecycle and contribute to building intelligent, scalable, and production-ready solutions. This is a multi-disciplinary role spanning AI/ML, Data Science, Data Engineering, Backend Engineering, Frontend Development, and AI-powered Automation. The ideal candidate should have strong depth in one or more areas while being comfortable working across the broader technology stack. You will work closely with Product, Engineering, Data Science, and business teams to transform complex problems into scalable AI-driven solutions.

The candidate will have responsibilities across the following functions:

AI / Machine Learning:

• Design, develop, and deploy AI/ML solutions for real-world business and healthcare use cases.
• Work with Generative AI, LLMs, NLP, RAG, AI agents, classification, extraction, and prediction use cases.
• Perform data analysis, feature engineering, model evaluation, and optimisation.
• Experiment with emerging AI models and frameworks and translate successful experiments into production solutions.
• Develop mechanisms to evaluate, monitor, and improve AI/ML model performance.

Data Engineering:

• Build and maintain scalable pipelines for data ingestion, transformation, validation, and processing.
• Work with structured, semi-structured, and unstructured data from multiple sources.
• Implement data quality, validation, and transformation processes.
• Optimise data processing workflows for performance, scalability, and reliability.

Backend Engineering:

• Develop scalable backend services and APIs for AI-powered applications.
• Integrate ML/AI models into production systems.
• Build microservices and data-intensive applications.
• Work with databases, APIs, messaging systems, and cloud infrastructure.
• Ensure solutions are secure, reliable, scalable, and production-ready.

Frontend Engineering:

• Develop intuitive interfaces for AI-powered applications and workflows.
• Build dashboards, data visualisations, and workflow-based applications.
• Integrate frontend applications with backend APIs and AI services.

AI Automation:

• Identify manual and repetitive business processes that can be automated using AI.
• Build AI-powered automation workflows, intelligent agents, and agentic solutions
• Integrate LLMs with APIs, databases, enterprise applications, and business workflows.
• Build solutions that can reason, retrieve information, execute actions, and provide meaningful outputs.
• Continuously improve automation workflows based on accuracy, performance, and user feedback.

Requirements:

• 8-12 years of experience in AI/ML, Software Engineering, Data Engineering, Data Science, or a related field.
• Strong programming skills in Python.
• Good understanding of Machine Learning, Generative AI, LLMs, NLP, and AI application development.
• Experience working with SQL, databases, APIs, and data processing pipelines.
• Strong understanding of software engineering fundamentals, including data structures, algorithms, design patterns, and object-oriented programming.
• Experience developing REST APIs and backend services.
• Familiarity with frontend technologies such as React, JavaScript/TypeScript, HTML, and CSS is a plus.
• Experience with Git, Docker, CI/CD, and cloud environments.
• Strong analytical, problem-solving, and debugging skills.
• Ability to work across multiple technical domains and quickly learn new technologies.

Good to Have:

• Experience with OpenAI, Anthropic, Gemini, or other LLM APIs.
• Hands-on experience with LangChain, LangGraph, LlamaIndex, Hugging Face, or similar frameworks.
• Experience building RAG pipelines, AI agents, or multi-agent systems.
• Knowledge of vector databases such as Pinecone, Milvus, Weaviate, or FAISS.
• Experience with Spark, Kafka, Airflow, Databricks, Snowflake, or similar data technologies.
• Experience with AWS, Azure, or GCP.
• Knowledge of Docker/Kubernetes and cloud-native application development.
• Experience working with large-scale or healthcare datasets.
• Understanding of AI evaluation, observability, model monitoring, and responsible AI practices.
• A full-stack AI mindset with the ability to move across data, AI models, backend, frontend, and automation.
• Strong engineering fundamentals with the ability to build production-grade AI solutions, not just prototypes.
• Ownership of solutions from problem definition through development, deployment, and optimisation.
• Curiosity and enthusiasm for emerging AI technologies.
• Ability to work in a fast-paced environment and collaborate effectively with Product, Engineering, Data Science, and business stakeholders.
• Strong communication, ownership, and problem-solving skills.