AI Engineer

About The Role
As an AI Engineer at Alaan (Bengaluru / Bangalore), you will own the development of production-grade AI applications spanning from early experimentation to deployment. This is not a pure research position; you must combine strong software engineering fundamentals with hands-on experience building apps using large language models.

You will solve problems such as extracting financial data, automating expense workflows, and building intelligent assistants for customer interaction.

Key Responsibilities

• Design, build, and deploy AI-powered features across Alaan's products.
• Create reliable workflows for prompt management, model orchestration, tool calling, and structured outputs.
• Build backend services and APIs that integrate AI capabilities into production applications.
• Develop evaluation frameworks to measure accuracy, relevance, reliability, latency, and cost of AI outputs.
• Experiment with different models, prompting techniques, embeddings, retrieval strategies, and fine-tuning approaches.
• Implement appropriate safeguards for sensitive financial and customer information.
• Monitor AI systems in production using user feedback to continuously improve performance.
• Collaborate cross-functionally with Product Managers, Designers, Data teams, and Backend Engineers.

Must Haves

• 2+ years of software engineering experience building AI/ML applications.
• Strong programming skills in Python and Node.js for production-grade AI apps.
• Hands-on experience with AWS SageMaker for ML/AI workloads.
• Proficiency with Jupyter Notebooks for data exploration and model development.
• Experience with Weights & Biases (W&B) for experiment tracking and versioning.
• Practical understanding of prompt engineering, embeddings, vector search, RAG, and LLM APIs.
• Familiarity with PostgreSQL, MongoDB, and at least one vector database like OpenSearch.
• Experience evaluating AI applications beyond manual testing.

Good To Haves

• Frameworks such as LangChain, PromptFoo, or similar AI orchestration tools.
• Snowflake experience for data warehousing/lakes.
• Fine-tuning open-source models and document intelligence/OCR experience.