The company is looking to shape the architecture and strategic direction of enterprise-grade AI solutions, specifically designing, building, and scaling both traditional ML and Generative AI (GenAI) solutions that are robust, production-ready, and aligned with business objectives.
Requirements
- Scalable architecture patterns for traditional ML and GenAI.
- Multi-cloud AI/ML services including AWS (SageMaker, Bedrock etc) and at least one of Azure (ML, OpenAI) or GCP (Vertex AI).
- Strong familiarity with multiple LLMs and embedding models (e.g., OpenAI, Anthropic, Meta, Google, Hugging Face).
- Proficiency in contextual memory and multiple vector databases for semantic search.
- MLOps and LLMOps practices, including CI/CD, model monitoring, versioning, drift detection, and governance.
- Prompt engineering and management practices, including prompt versioning, A/B testing of prompts, and experience with prompt management tools
- AI/ML observability stacks such as Weights & Biases, Langsmith or similar tools.
Responsibilities
- Lead the hands-on architecture, development, and deployment of production-grade AI/ML systems, ensuring scalability, reliability, performance, and cost-efficiency.
- Architect traditional ML solutions (e.g., classification, regression, recommendation systems) and advanced GenAI systems including Retrieval-Augmented Generation (RAG) and Agentic AI.
- Design and implement cloud-native AI/ML pipelines using cloud platforms.
- Evaluate, prototype, and build PoCs regularly to test architectural decisions, validate feasibility, and accelerate solution delivery.
- Integrate and deploy multiple LLMs (e.g., from OpenAI, Claude, Gemini, LLaMA, Hugging Face) and vector databases (e.g., Pinecone, Qdrant, pgvector, Milvus, Weaviate).
- Create reusable frameworks and solution templates that drive consistency, speed, and quality across AI initiatives.
- Ensure all solutions are aligned with responsible AI standards, security best practices, and enterprise governance.
Other
- Bachelor's degree in computer science, Engineering, or a related field (Master’s preferred).
- 8+ years of experience in AI/ML engineering or architecture roles.
- Strong portfolio of real-world deployments in both traditional ML and GenAI.
- Experience architecting agentic AI systems and multi-agent orchestration workflows.
- Experience in regulated industries, especially healthcare or finance.
