As a AI Solutions Architect, you will be the visionary lead responsible for designing and implementing our next-generation AI-driven infrastructure. You will bridge the gap between cutting-edge machine learning research and production-grade software engineering. Your expertise will be vital as you architect scalable systems that leverage Large Language Models (LLMs), Generative AI, and traditional ML to solve complex business problems, ensuring our technical stack is future-proof and ethically sound.

Responsibilities:

- System Architecture: Design and oversee the implementation of end-to-end AI architectures, including data pipelines, model orchestration, and scalable inference endpoints.
- AI Strategy: Lead the evaluation and selection of AI technologies (OpenAI, Anthropic, Hugging Face, LangChain) to optimize for performance, cost, and latency.
- LLM Integration: Architect robust frameworks for RAG (Retrieval-Augmented Generation), agentic workflows, and fine-tuning strategies to enhance model accuracy and relevance.
- Engineering Standards: Establish best practices for MLOps and LLMOps, ensuring seamless CI/CD for AI models and maintaining rigorous version control.
- Collaboration & Leadership: Partner with Data Scientists, Backend Engineers, and Product Leads to translate business goals into high-impact AI features.
 -Governance: Implement security protocols and ethical guardrails for AI usage, focusing on data privacy, bias mitigation, and cost management.

Required Skills and Qualifications:

- Experience in software architecture, with at least 3+ years specifically focused on deploying AI/ML solutions at scale.
- AI Expertise: Deep proficiency in Generative AI, Vector Databases (e.g., Pinecone, Milvus, Weaviate), and framework orchestration (e.g., LangChain, LlamaIndex).
- Core Languages: Expert-level coding skills in Python and familiarity with modern backend stacks (C#/.NET, Go, or Java).
- Data Engineering: Experience with large-scale data processing and storage (Spark, Snowflake, or Databricks).
- Infrastructure: Mastery of cloud-native AI services in AWS (Sagemaker, Bedrock), Azure (Azure OpenAI), or GCP (Vertex AI).
- Language: Advanced English proficiency (fluent verbal and written communication).

Nice-to-have Skills:

- Optimization: Knowledge of model quantization, pruning, and inference optimization (e.g., vLLM, TensorRT).
- DevOps/MLOps: Experience with Kubernetes (K8s), Docker, and MLflow for model lifecycle management.
- Advanced Research: Strong understanding of Transformer architectures, neural network design, and the latest peer-reviewed AI research.
- Security: Experience with AI-specific security threats (e.g., prompt injection, data poisoning).
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, or a related quantitative field.