Description

Lead the design, architecture, and implementation of enterprise-scale Generative AI, Machine Learning, and Agentic AI solutions.

This role is remote.

Responsibilities

• Architect and develop AI-powered applications using LLMs, RAG frameworks, AI Agents, and Vector Databases.
• Design end-to-end ML pipelines covering data ingestion, feature engineering, model training, deployment, monitoring, and optimization.
• Build scalable backend AI services and APIs using Python, microservices, and cloud-native technologies.
• Implement MLOps frameworks for model versioning, deployment automation, governance, and observability.
• Mentor AI/ML engineers, establish engineering standards, and drive enterprise AI adoption strategies.

Required Skills

• 5+ years of experience leading enterprise AI/ML engineering teams.
• Strong expertise in Generative AI, LLMs (OpenAI, Azure, Anthropic), Prompt Engineering, and Fine-Tuning.
• Hands-on experience with Agentic AI frameworks like LangChain, LangGraph, CrewAI, or AutoGen.
• Proficiency in Python, Pandas, NumPy, FastAPI, and REST APIs.
• Experience with Vector Databases (Pinecone, FAISS, ChromaDB, Weaviate, Milvus) and RAG systems.
• Cloud platform experience with AWS (Bedrock, SageMaker), Azure, or GCP.
• Knowledge of MLOps tools including MLflow, Kubeflow, Docker, and Kubernetes.
• Experience with Big Data technologies such as Databricks, Apache Spark, PySpark, Snowflake, Data Lakes, and Data Warehousing.

Preferred Skills

• Experience building enterprise AI Copilots, Autonomous Agent Systems, and Multi-Agent Architectures.
• Exposure to Responsible AI, AI Security, and Governance frameworks.
• Relevant certifications in Azure AI Engineer, AWS Machine Learning, Databricks AI, or Google AI.

Key Skills

Education

Bachelor's degree