Senior Process Manager

AI & Generative AI

• Large Language Models (OpenAI, Claude, Gemini, Llama, Mistral)
• Generative AI and Agentic AI architectures
• Retrieval-Augmented Generation (RAG)
• Prompt Engineering
• Multi-Agent Systems
• AI Orchestration Frameworks (LangChain, LangGraph, CrewAI, AutoGen)
• Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS)

Machine Learning & Data Science

• Machine Learning and Deep Learning fundamentals
• Model evaluation, deployment, and monitoring
• Feature engineering and data pipelines

Programming & Cloud

• Python (mandatory)
• AWS, Azure, or GCP
• REST APIs and Microservices
• MLOps / LLMOps
• Docker and Kubernetes

Architecture & Consulting

• Enterprise Solution Architecture
• Technical Consulting and Pre-Sales
• Requirements Gathering and Solution Design
• Stakeholder Management
• Executive Presentations and Customer Workshops

Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 10–15+ years of experience in software engineering, AI/ML, data science, or solution architecture.
• Proven experience delivering enterprise AI/GenAI solutions in production environments.
• Strong customer-facing and leadership experience.

Preferred Qualifications

• Certifications in AWS, Azure, GCP, or Generative AI.
• Experience with Responsible AI, AI Governance, and Security.
• Exposure to Telecom, BFSI, Retail, Healthcare, Manufacturing, or Supply Chain domains.

• Lead end-to-end AI solutioning activities, including discovery, architecture, design, development, and deployment.
• Engage with business stakeholders, customers, and leadership teams to identify AI opportunities and define solution roadmaps.
• Design and implement Generative AI, Agentic AI, and Machine Learning solutions aligned with business objectives.
• Architect enterprise-grade AI applications leveraging LLMs, RAG frameworks, AI agents, vector databases, and cloud platforms.
• Collaborate with pre-sales and delivery teams for solution proposals, effort estimation, and technical presentations.
• Define scalable AI architecture, governance frameworks, security controls, and best practices.
• Guide engineering teams through implementation, code reviews, and deployment strategies.
• Drive innovation by evaluating emerging AI technologies, frameworks, and industry trends.
• Mentor AI engineers, data scientists, and architects while fostering technical excellence.