AI Engineer

The core responsibilities for the job include the following:

AI Application Development:

• Design, build, and deploy AI-powered applications using large language models, multimodal models, and intelligent agents.
• Develop scalable AI solutions that automate enterprise workflows and enhance customer experiences.
• Build reusable AI services, APIs, and components that integrate seamlessly into the platform.
• Rapidly prototype, validate, and productionize AI use cases across multiple industries.

LLM & Agent Engineering:

• Develop advanced prompt engineering strategies, Retrieval-Augmented Generation (RAG) pipelines, and agentic workflows.
• Build multi-agent systems capable of reasoning, planning, and executing complex enterprise tasks.
• Integrate foundation models from leading providers while optimizing performance, reliability, and cost.
• Evaluate, fine-tune, and optimize AI models for production use cases.

AI Platform Integration:

• Integrate AI applications with enterprise systems, databases, APIs, and third-party platforms.
• Build scalable inference pipelines and AI workflows that support production deployments.
• Collaborate with platform and infrastructure teams to optimize model serving, orchestration, and deployment.
• Implement robust evaluation frameworks to continuously measure AI quality and performance.

Performance & Reliability:

• Optimize AI applications for latency, accuracy, scalability, and operational efficiency.
• Implement observability, monitoring, logging, and evaluation pipelines for AI systems.
• Ensure AI solutions meet enterprise requirements for security, privacy, governance, and compliance.
• Troubleshoot production AI systems and continuously improve model performance through experimentation and feedback.

Engineering Excellence:

• Write clean, maintainable, and production-quality code following engineering best practices.
• Conduct code reviews and contribute to technical design discussions.
• Collaborate closely with Product, Platform, Infrastructure, and Customer Success teams to deliver customer-centric AI solutions.
• Stay current with emerging AI research, tools, and technologies, and evaluate their applicability to the platform.

Requirements:

• 3+ years of experience building AI, machine learning, or backend software applications.
• Strong proficiency in Python with experience building production-grade applications.
• Hands-on experience with large language models, prompt engineering, Retrieval-Augmented Generation (RAG), and AI agent frameworks.
• Experience using frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, LangGraph, or similar.
• Familiarity with vector databases such as Pinecone, Weaviate, Milvus, Chroma, or PGVector.
• Experience integrating AI applications with REST APIs, databases, and cloud services.
• Understanding of model evaluation, prompt optimization, hallucination mitigation, and AI quality measurement.
• Experience with Docker, Kubernetes, and cloud platforms such as AWS, Azure, or GCP.
• Familiarity with Git, CI/CD pipelines, and software engineering best practices.
• Understanding of distributed systems, scalable APIs, and enterprise application development.
• Exposure to fine-tuning, model serving, MLOps, or inference infrastructure is highly desirable.

Bonus/Good to Have:

• Generative AI: Experience building enterprise applications using GPT, Claude, Gemini, Llama, Mistral, or other foundation models.
• AI Infrastructure: Experience with vLLM, TensorRT-LLM, TGI, model deployment, GPU optimization, or inference platforms.
• Machine Learning: Experience with supervised learning, deep learning, model training, evaluation pipelines, and ML frameworks such as PyTorch or TensorFlow.
• Enterprise Integrations: Experience integrating AI solutions with CRMs, ERPs, productivity tools, cloud platforms, and enterprise workflows.
• Developer Platforms: Experience building SDKs, APIs, developer tools, or AI platform capabilities for external developers.

Preferred Attributes:

• Founder-level ownership and bias for action.
• Strong strategic thinking and ability to connect technical decisions to business impact.
• Excellent communication and mentoring skills.
• Thrives in ambiguity, fast-paced environments, and early-stage startup culture.