Title: Senior Principal AI/ML Architect
Location: [Pune - Hybrid]
Employment Type: Full-Time
Role Overview
SaamaAI are seeking a visionary Senior Principal AI/ML Architect to lead the technical design, strategy, and deployment of our enterprise-scale Artificial Intelligence ecosystems. As an industry leader, you will bridge the gap between cutting-edge AI research and production-grade business solutions.
This role demands deep expertise in both Classical Machine Learning and Modern Generative AI. You will be the chief architect responsible for designing autonomous agentic workflows, advanced Retrieval-Augmented Generation (RAG) pipelines, and highly resilient cloud-native AI systems across AWS, Azure, and GCP. You will tackle modern data architecture challenges head-on, ensuring our foundational data platforms are secure, scalable, and optimized for AI-first workloads.
Key Responsibilities
- Enterprise AI & GenAI Architecture:
- End-to-End System Design: Architect scalable, low-latency, and highly available AI/ML systems that seamlessly integrate Classical ML (predictive modeling, clustering, time-series) with Generative AI (LLMs, multimodal models).
- Agentic Frameworks & Multi-Agent Systems: Design autonomous and semi-autonomous AI agents using modern frameworks (e.g., LangGraph, AutoGen, CrewAI). Architect complex agentic reasoning workflows involving tool-use, multi-step planning, and dynamic execution.
- State Management & Memory: Engineer advanced conversational memory architectures (short-term, long-term, semantic memory) and robust state management protocols for long-running agentic tasks.
- Advanced RAG & Hallucination Mitigation: Build highly accurate RAG pipelines utilizing vector databases (Pinecone, Milvus, Weaviate), hybrid search algorithms, and semantic reranking. Implement rigorous guardrails, self-correction loops, and grounding techniques to systematically eliminate LLM hallucinations.
- Cloud & Modern Data Platforms:
- Multi-Cloud AI Infrastructure: Lead architecture decisions and deployment strategies across AWS (Bedrock, SageMaker, EKS), Azure (OpenAI, ML Studio, AI Search), and GCP (Vertex AI, BigQuery ML).
- Modern Data Architecture: Solve complex data engineering challenges, including streaming vs. batch ingestion, unstructured data processing, and scalable embedding generation. Architect solutions natively integrated with modern data platforms (e.g., Snowflake, Databricks, Apache Kafka).
- Data Mesh & Governance: Define data contracts and implement architectures that ensure clean, compliant, and governed data pipelines to feed AI models in real-time.
- Production-Grade Engineering & LLMOps:
- Scalability & Resilience: Translate PoCs into robust, scalable, and fault-tolerant production systems. Establish architectural patterns for high-throughput model serving, caching, API gateways, and load balancing.
- MLOps & LLMOps: Establish enterprise standards for CI/CD in machine learning. Oversee automated model evaluation, continuous fine-tuning (LoRA, PEFT), prompt lifecycle management, drift detection, and system observability.
- Security, Compliance, & Responsible AI:
- Security by Design: Embed zero-trust architecture, data encryption (at rest and in transit), Role-Based Access Control (RBAC), and Data Loss Prevention (DLP) natively into the AI ecosystem.
- AI Governance: Ensure all AI models and systems comply with global regulatory standards (ISO/IEC 42001, NIST AI RMF, EU AI Act, SOC2, HIPAA).
- Ethical AI: Implement bias detection mechanisms, explainability protocols (XAI), and human-in-the-loop (HITL) fallback strategies.
Required Qualifications & Technical Expertise
- Experience: 12+ years of software engineering and architecture experience, with a minimum of 5+ years dedicated to AI/ML architecture and deploying models into production environments.
- Generative AI & LLMs: Deep understanding of foundational models (GPT, Claude, Llama , Gemini). Hands-on experience with fine-tuning methodologies, prompt engineering, and embedding generation.
- Agentic & Orchestration Frameworks: Master-level proficiency in LangChain, LlamaIndex, LangGraph, AutoGen, or equivalent ecosystem tools.
- Cloud Native Mastery: Extensive architectural experience across AWS, Azure, and/or GCP. Strong command over containerization and orchestration (Docker, Kubernetes/EKS/AKS/GKE).
- Modern Data Stack: Proficiency with vector databases, graph databases (Neo4j), and modern data warehousing/lakehouse architectures (Databricks, Snowflake).
- Programming Languages: Expert-level Python; strong proficiency in Go, Java, or C++ is highly preferred.
- Leadership & Vision: Exceptional ability to communicate complex architectural tradeoffs to C-level executives, while also being capable of doing deep-dive code and architecture reviews with senior engineering teams.
(ref:hirist.tech)
