Role Overview

We are seeking a highly hands-on Senior GenAI Lead with 8+ years of experience in AI/ML engineering and enterprise system delivery. This role combines deep machine learning expertise, modern Generative AI architecture, and strong technical leadership to build scalable, production-ready AI platforms.

The ideal candidate brings strong foundations in classical machine learning, predictive modeling, and data science, combined with hands-on experience in LLM orchestration, multi-agent systems, and enterprise AI modernization.

This is a technical leadership role focused on architecture, execution, engineering rigor, and mentorship.

Experience Requirements:

• 8+ years of experience in AI/ML engineering, data science, and enterprise software systems
• 3+ years building production-grade Generative AI and LLM-based systems
• Proven experience delivering ML and AI solutions in one or more domains like manufacturing, retail, healthcare, logistics, or enterprise domains.

Key Responsibilities – Technical Architecture & AI System Design:

• Architect and implement enterprise-grade GenAI platforms using LLMs and multi-agent orchestration frameworks (Langgraph, CrewAI, Agent2Agent, MCP, etc.)
• Design scalable RAG architectures using vector databases and structured knowledge systems
• Build hybrid AI systems integrating predictive ML models with GenAI copilots
• Lead cloud-native AI deployments across Azure, AWS, and GCP

Core Machine Learning & Advanced Analytics:

• Design, develop, and deploy classical ML models including regression, classification, forecasting, churn prediction, anomaly detection, and optimization
• Perform EDA, feature engineering, model evaluation, and productionization
• Implement model monitoring, validation frameworks, and retraining strategies
• Integrate ML models into GenAI workflows for decision intelligence
• Apply statistical rigor and business KPIs to measure model impact

Hands-On Engineering & Delivery:

• Develop Python-based AI systems with strong coding standards
• Build and review agentic AI workflows and modernization automation frameworks
• Implement test-driven validation pipelines and data validation systems
• Ensure scalability, resilience, and cost-efficient AI deployments
• Own end-to-end delivery from architecture to production rollout

Responsible AI & Governance:

• Integrate Responsible AI features such as prompt shields, groundedness detection, and risk monitoring
• Design governance frameworks for enterprise AI deployments
• Ensure compliance with enterprise IT and security standards

Technical Mentorship & Engineering Excellence:

• Mentor AI engineers and data scientists in multi-agent architecture and ML best practices
• Conduct code reviews and architecture reviews
• Promote reproducibility, testing discipline, and failure-mode design

Required Technical Skills:

Generative AI & Agentic Systems:

• LLM orchestration frameworks (CrewAI, AutoGen, LangGraph)
• RAG architecture design
• Vector databases
• Prompt engineering & context management

Machine Learning:

• Supervised & unsupervised learning
• Forecasting & time-series modeling
• Classification & regression modeling
• Feature engineering & model validation
• ML pipeline design & monitoring
• Statistical evaluation techniques

Engineering & Cloud:

• Strong Python programming
• Azure / AWS / GCP experience
• API-based integration of AI systems
• Production deployment and CI/CD workflows

What Success Looks Like:

• Production-grade AI systems deployed with measurable business impact
• Reliable multi-agent orchestration pipelines with validation and governance layers
• ML models that are statistically sound and business-aligned
• Teams enabled to independently extend AI systems using strong architecture patterns
• AI platforms built with resilience, monitoring, and explicit failure handling