AI Developer

Role & responsibilities:

• 4+ years of experience in AI Development with at least 2+ years of hands-on experience in AI/ML, Generative AI, and LLM-based applications. Experience designing, developing, and deploying intelligent AI solutions, including agentic and multi-agent systems.
  
• Strong programming expertise in Python, with hands-on experience building scalable AI applications, APIs, automation frameworks, and reusable AI components.
  
• Experience working with Large Language Models (LLMs) such as OpenAI, Azure OpenAI, Anthropic, Gemini, or open-source models, including:
  
• Hands-on experience designing and developing AI agents and orchestration workflows using frameworks such as LangChain, LangGraph, LlamaIndex, DSPy, OpenAI Assistants, Azure OpenAI, or similar technologies.
  
• Strong understanding of distributed and cloud-native application architectures, including microservices, event-driven systems, REST APIs, and message queues.
  
• Experience integrating AI solutions with enterprise systems and data platforms, including vector databases, knowledge bases, and structured/unstructured data sources.
  
• Proficiency with CI/CD pipelines (GitHub Actions, Azure DevOps), software development best practices, version control, testing automation, and deployment processes.
  
• Experience with observability and monitoring tools such as OpenTelemetry, Prometheus, Grafana, Datadog, or similar platforms to track AI application performance and reliability.
  
• Solid understanding of AI security, privacy, governance, and compliance, including PII handling, model safety, responsible AI principles, and content moderation frameworks.
  
• Strong analytical, problem-solving, communication, and leadership skills, with the ability to collaborate effectively across Engineering, Data Science, Product, and Operations teams.
  

Preferred Qualifications:

• Experience building and deploying multi-agent AI systems, autonomous workflows, AI copilots, and conversational AI applications.
  
• Knowledge of MLOps practices, including model lifecycle management, versioning, experiment tracking, evaluation pipelines, and continuous model improvement.
  
• Familiarity with RAG architectures, vector databases (Pinecone, Weaviate, Chroma, FAISS, Azure AI Search), and knowledge graph integrations.
  
• Experience with cloud platforms (AWS, Azure, or GCP), serverless computing, containerization (Docker, Kubernetes), and scalable AI infrastructure.
  
• Hands-on experience with A/B testing, model evaluation frameworks, and AI performance optimization for production-grade applications.
  
• Experience managing cost optimization, latency, scalability, reliability, and SLA requirements for AI workloads in enterprise environments.
  
• Exposure to advanced AI technologies such as Agentic AI, Multimodal AI, Computer Vision, NLP, and Generative AI platforms.
  
• Prior experience delivering AI-driven solutions in production environments and mentoring junior developers or technical teams.