Job Summary

Role Summary

The AI Solution Lead is a hands-on technical leadership role responsible for shaping, designing, and driving end-to-end AI and Generative AI solutions for enterprise customers. This role sits at the intersection of business problem framing, AI architecture, and delivery execution, owning the translation of real business use cases into scalable, production-grade AI systems. The AI Solution Lead ensures that AI solutions are technically sound, economically viable, secure, observable, and aligned to measurable business outcomes.

Key Responsibilities

Key Responsibilities

• Lead end-to-end AI solutioning from problem discovery through architecture design, PoC/MVP, and production rollout.
• Break down complex business problems into well-defined AI use cases with clear success metrics and KPIs.
• Design solution architectures involving LLMs, RAG pipelines, agent-based workflows, and enterprise system integrations.
• Define data, model, prompt, and orchestration strategies required to meet accuracy, latency, cost, and reliability targets.
• Select appropriate AI models (commercial and open-source), deployment patterns, and cloud services based on use case constraints.
• Guide teams on AI non-functional requirements including scalability, observability, security, cost optimization, and compliance.
• Work closely with AI Architects and LLMOps teams to ensure production readiness and operational excellence.
• Drive technical governance, design reviews, and architectural decision-making across AI initiatives.
• Support client conversations, solution walkthroughs, estimations, and technical risk articulation.

Skill Requirements

Technical Skills & Architecture Expertise

• Strong understanding of AI and Generative AI architectures, including LLMs, embeddings, vector databases, and retrieval systems.
• Hands-on experience designing Retrieval-Augmented Generation (RAG) pipelines and grounding strategies.
• Deep understanding of agentic AI patterns: tool/function calling, planners, memory, and multi-agent workflows.
• Experience with LLM platforms such as Azure OpenAI, OpenAI, Anthropic, Google Vertex AI, or equivalent.
• Strong backend engineering or architecture foundation (Java / Python / APIs / microservices).
• Knowledge of distributed systems, asynchronous processing, and event-driven architectures.
• Familiarity with AI observability concepts: quality metrics, drift detection, traceability, and cost monitoring.
• Awareness of Responsible AI, security, data privacy, and regulatory considerations in AI deployments.

Other Requirements

Tools & Platforms

• Programming languages: Python (mandatory), Java or equivalent backend languages.
• AI frameworks and orchestration: LangChain, LlamaIndex, LangGraph, or similar.
• Vector databases: Pinecone, FAISS, Weaviate, Azure AI Search, or equivalent.
• Cloud platforms: Azure (preferred), AWS, GCP.
• Containerization and deployment: Docker, Kubernetes (working knowledge).
• Observability and monitoring: OpenTelemetry concepts, dashboards, and metrics.

Collaboration & Leadership

The AI Solution Lead collaborates closely with product owners, business stakeholders, AI Architects, Engineers, LLMOps, Observability, and Responsible AI teams. This role provides technical direction, mentors engineering teams, and acts as the single point of accountability for solution correctness, scalability, and business impact.

Experience & Qualifications

• 8–12 years of overall experience in software engineering, architecture, or solution engineering roles.
• 3–5+ years of direct experience designing or leading AI / ML / Generative AI solutions in enterprise environments.
• Proven experience owning solution architecture and delivery across multiple client engagements.