Responsibilities :

- Driving AI Strategy: Define and execute the organizational AI roadmap, ensuring alignment with long-term business goals and emerging technology trends.

- Use Case Identification: Identify, evaluate, and prioritize high-impact AI and Gen AI use cases across the organization.

- End-to-End Delivery: Take full ownership of the lifecycle of AI applications, from conceptual design and prototyping to production deployment and monitoring.

- Solution Architecture: Lead the planning and designing of scalable AI solutions, specifically focusing on Retrieval-Augmented Generation (RAG) and complex Agentic workflows.

- Cost-Benefit Analysis: Conduct detailed financial evaluations for AI initiatives, balancing token costs and infrastructure overhead against projected business value.

- Governance & Ethics: Establish and maintain a robust Responsible AI and AI governance framework to ensure ethical compliance, data privacy, and security.

- Change Management: Act as a primary driver for organizational change, promoting AI adoption and literacy across both technical and non-technical teams.

- Mentorship & Coaching: Provide technical leadership, high-level code reviews, and career development guidance to engineering team members.

- Quality Assurance: Ensure the delivery of high-quality, secure, and performant AI architectures that adhere to enterprise coding standards and best practices.

- LLMOps Lifecycle Management: Implement and oversee the full LLMOps lifecycle, including automated evaluation pipelines, model versioning, and performance monitoring.

- AI FinOps & Cost Governance: Establish practices to track token consumption, optimize model routing based on cost, and provide accurate forecasting for AI spend.

- Vendor-to-In-house Handover Leadership: Lead the technical transition of AI/ML assets from third-party vendors to in-house teams, ensuring architectural alignment.

- Resilient AI System Design: Architect multi-model failover strategies and routing mechanisms to ensure 24/7 availability for mission-critical services.

- Human-in-the-Loop (HITL) Orchestration: Design and implement HITL frameworks for high-stakes AI-driven decisions to ensure a final layer of human verification.

- AI Platform Engineering: Develop reusable AI building blocks, internal SDKs, and standardized API gateways to accelerate AI feature delivery.

- Red Teaming & Security: Lead AI-specific security initiatives, including prompt injection testing, data exfiltration defense, and secure sandboxing.

- Evaluation & Release Gates: Define rigorous release gates based on automated scoring for toxicity, hallucination, and factual accuracy.

- Enterprise Knowledge Architecture: Oversee the design and scaling of vector databases and knowledge graphs that serve as the ground truth for RAG systems.

- Regulatory Compliance Alignment: Ensure AI implementations comply with international standards and emerging regulations like the EU AI Act or NIST AI RMF.

- Open to travel outside India as per project requirements.

Mandatory Skills :

- Expert Python mastery including advanced data structures, asynchronous programming, and performance optimization for high-throughput AI production pipelines.

- Advanced proficiency in Object-Oriented Programming using Java, C#, or TypeScript with deep knowledge of Clean Architecture and SOLID design principles.

- Hands-on experience with core AI/ML foundation libraries including Pandas for data manipulation, NumPy for computation, and Sci-Kit Learn for modeling.

- Technical visualization and statistical reporting skills using Matplotlib and Seaborn to communicate complex model insights and performance to stakeholders.

- Deep learning expertise in designing and fine-tuning neural networks using PyTorch or TensorFlow to optimize models for real-world enterprise applications.

- Proficiency in LLM integration and lifecycle management using Hugging Face Transformers, covering fine-tuning, quantization, and deployment strategies.

- Advanced architecting of RAG systems for complex data orchestration, intelligent indexing, and high-performance information retrieval.

- Experience building autonomous agents with Microsoft Agent Framework or Google Agent Development Kit (ADK) to automate complex multi-step business tasks.

- Orchestration of multi-agent workflows and reasoning chains using LangGraph or CrewAI to facilitate collaborative AI behaviors and structured execution.

- Mastery of Model Context Protocol (MCP) to standardize how AI agents interact with external tools, secure APIs, and enterprise-wide data environments.