AI Architect

· GenAI Stack & Frameworks: Deep hands-on experience with LLM orchestration (LangChain, LlamaIndex, AutoGen), RAG architectures, fine-tuning techniques (LoRA/PEFT), vector search (Pinecone, Qdrant, Milvus, pgvector), and Agentic tool-use protocols (MCP).

· Cloud & Infrastructure: Advanced architecture experience on AWS (Amazon Bedrock, SageMaker, OpenSearch, ECS/EKS, Lambda).

· Enterprise Data & Analytics: Hands-on integration experience with enterprise data platforms such as Snowflake, Databricks, Apache Spark, and real-time streaming architectures.

· Core Engineering: Strong foundation in Python, Java/C++, API design (REST/gRPC), and microservices architecture.

2. Experience & Qualifications

· Total Experience: 12+ years in Enterprise Software Engineering & Architecture, with 4+ years dedicated to AI/ML and 2+ years designing LLM/GenAI production systems.

· Domain Experience: Prior experience in Financial Services, Asset Management, Investment Banking, or Fintech is highly preferred.

· Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or related engineering discipline.

· Certifications (Preferred): AWS Certified Solutions Architect (Professional), AWS Certified Machine Learning – Specialty, or TOGAF certification.

3. Soft Skills & Leadership

· Exceptional ability to translate complex AI technicalities into clear, executive-ready strategy documents and presentations.

· Pragmatic balance between technical innovation, cost optimization (token/compute economics), and strict regulatory controls.