Design, build, and maintain production-grade AI applications using LLMs, RAG, vector databases, and agent frameworks. Develop cloud-native microservices, APIs, and CI/CD pipelines; implement monitoring, evaluation, and model management. Collaborate with product and platform teams, mentor engineers, and drive engineering best practices and AI governance.
Key Responsibilities
AI Application Development
- Design, develop, and maintain production-grade AI-powered applications and services.
- Build and integrate solutions leveraging LLMs, generative AI platforms, vector databases, embeddings, and agent frameworks.
- Implement Retrieval-Augmented Generation (RAG) architectures and knowledge retrieval systems.
- Develop prompt engineering strategies, evaluation frameworks, and guardrails to improve AI quality and reliability.
- Integrate AI capabilities into existing products, workflows, and business processes.
Software Engineering & Architecture
- Design scalable, secure, and maintainable software systems using modern engineering practices.
- Develop APIs, microservices, and cloud-native applications supporting AI workloads.
- Create reusable frameworks and components that accelerate AI product development.
- Ensure solutions meet performance, reliability, observability, and security standards.
- Participate in architecture reviews and technical design discussions.
AI Operations & Model Management
- Evaluate, benchmark, and optimize AI models and providers for quality, latency, and cost.
- Implement monitoring, logging, testing, and observability for AI systems. Establish processes for model evaluation, prompt versioning, and continuous improvement.
- Collaborate with data and platform teams to manage AI infrastructure and deployment pipelines.
Collaboration & Leadership
- Partner with product management to identify and prioritize AI opportunities.
- Mentor engineers and contribute to team-wide technical growth.
- Drive engineering best practices, code quality, and architectural standards.
- Participate in code reviews and provide technical leadership on complex initiatives.
- Communicate technical concepts effectively to both technical and non-technical stakeholders.
Required Qualifications
- Bachelor's degree in Computer Science, Software Engineering, or related field, or equivalent practical experience.
- 5+ years of professional software engineering experience.
- 2+ years developing AI, machine learning, or generative AI applications.
- Strong proficiency in one or more modern programming languages such as Python, TypeScript, Java, or C#.
- Experience building and deploying cloud-native applications on AWS, Azure, or Google Cloud.
- Experience with coding harnesses
- Experience with RESTful APIs, distributed systems, and microservice architectures.
- Hands-on experience with LLMs and AI platforms such as OpenAI, Anthropic, Azure OpenAI, Google Gemini, or similar.
- Experience implementing RAG solutions, vector databases, and semantic search.
- Strong understanding of software development lifecycle, CI/CD, testing, and DevOps practices.
- Excellent problem-solving, communication, and collaboration skills.
Preferred Qualifications
- Experience with agentic AI frameworks such as Strands, LangChain, LangGraph, Semantic Kernel, CrewAI, or similar.
- Experience with vector databases such as Pinecone, Weaviate, Qdrant, Chroma, or Azure AI Search.
- Familiarity with MLOps and AI evaluation frameworks.
- Experience deploying AI workloads using Kubernetes and containerized environments.
- Knowledge of AI governance, security, compliance, and responsible AI practices.
- Experience in healthcare, fintech, SaaS, or other regulated industries.
- Contributions to open-source AI projects or technical communities.
Equal Opportunity Employer
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