Senior AI Software Engineer

Job Responsibilities

• Development: Design, develop, ship, and maintain applications using modern frameworks and technologies. Own roadmaps, plans, and timelines. Lead architectural discussions and make technical decisions. Work on both rapid prototypes and enterprise-quality, high-scale products.
• AI Applications: Select and deploy foundation models and LLMs as a service. Write and refine prompts. Measure, evaluate, and optimize model performance. Integrate models into workflows. Extend model capabilities with memory, tools, guardrails, and multi-agent patterns. Acquire, clean, and synthesize data sets. Partner with domain experts to capture knowledge. Stay current with AI trends and apply them to solve real-world problems.
• Cloud: Leverage the tools and services of a major cloud provider. Build APIs, microservices, and serverless functions to support application functionality. Manage our cloud environment and CI/CD pipelines. Make tradeoffs around performance and cost.
• Communications: Partner with business leaders in India, US, and Europe. Clearly communicate status, technical challenges, tradeoffs, and insights.
  
  

• Leadership: Build relationships, raise issues, define alternatives, and uncover resources. Establish practices, processes, and culture for a growing team.
  
  

• Education: Bachelor’s in Computer Science, Information Technology, AI/ML, or Data Science fields. Masters preferred.
• Experience: 5+ years of experience as a Software Engineer, as an AI/ML, or in a similar role.
• Development: Proficient in Python or JavaScript/Typescript. Java/Go/C++ nice to have. Hands-on back-end experience. Familiarity with common front-end technologies. Familiarity with relational, non-relational, and vector databases. Comfort with Agile processes.
• AI: More than one year of engineering experience (including relevant projects) building production-quality applications leveraging LLMs. Experience with frameworks for AI applications (e.g., LangChain, LlamaIndex, Haystack). Knowledge of deep learning and ML frameworks (e.g., TensorFlow, PyTorch). Experience managing data sets. Familiarity with MLOps.
• Cloud: Experience with cloud platforms (e.g., AWS, Azure, or GCP). Solid understanding of RESTful APIs, microservices architecture, and containerization.
• Soft skills: Excellent communication and collaboration skills. Results-driven, entrepreneurial, and comfortable working from 0→1.