Job Overview

Lead the architecture, design and deployment of Generative AI, Agentic AI, ML and deep learning solutions end to end: data pipelines, retrieval, agent orchestration, model serving, evaluation and monitoring on AWS Bedrock, Azure OpenAI and Databricks.

Roles & Responsibility:

Solution Architecture

• Own and lead the enterprise AI reference architecture and end-to-end design for Generative AI and Agentic AI solutions, covering data ingestion, retrieval, orchestration, model serving, security, and monitoring.
• Select the optimal models, platforms, and architectural patterns across AWS Bedrock, Azure OpenAI, and Databricks, balancing accuracy, latency, scalability, cost, and compliance requirements.
• Develop architecture blueprints, reference designs, and technical decision records (TDRs).
• Lead architecture reviews and establish engineering, security, and coding standards for AI solution delivery.

Generative AI & Agentic AI

• Design and implement multi-agent systems using frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, and Amazon Bedrock Agents.
• Build enterprise-grade RAG solutions leveraging chunking strategies, embeddings, hybrid search, semantic search, re-ranking, and vector databases.
• Implement prompt engineering, guardrails, structured outputs, tool/function calling, and Model Context Protocol (MCP) integrations.
• Establish LLMOps practices including evaluation frameworks, prompt versioning, groundedness validation, tracing, monitoring, and cost optimization.

Data Science, Machine Learning & Deep Learning

• Develop machine learning and deep learning solutions for forecasting, classification, anomaly detection, optimization, NLP, and predictive analytics.
• Build scalable data and feature engineering pipelines using Databricks, PySpark, Delta Lake, Unity Catalog, and MLflow.
• Deploy AI models and intelligent agents as scalable APIs using FastAPI, Docker, Kubernetes, and CI/CD pipelines.

Qualifications

Must Have

• 7+ years of experience in Data Science, Machine Learning, or AI Engineering, including 3+ years architecting production GenAI/LLM solutions.
• Proven experience designing and deploying Agentic AI or Multi-Agent systems.
• Hands-on experience with at least two of AWS Bedrock, Azure OpenAI, and Databricks.
• Strong expertise in Python, SQL, and PySpark with solid ML and statistical fundamentals.
• Experience deploying AI solutions using FastAPI, Docker, Kubernetes, and CI/CD pipelines.

Good to Have

• Experience with LLM fine-tuning (LoRA/PEFT) and evaluation frameworks.
• Exposure to Knowledge Graphs, Document AI, OCR, or Computer Vision.
• Experience with engineering data domains (BOM, PDM, SAP) or financial risk modeling (Credit Risk, IFRS 9).
• Certifications such as AWS Certified Machine Learning, Azure AI Engineer Associate, or Databricks Generative AI Engineer.

Competencies