AI Practice Head

Role Summary: We are seeking an accomplished AI Lead to drive our AI practice end to end from client conversations and use-case discovery through architecture, delivery, and team leadership. You will be the go-to expert for all things AI/ML: classical machine learning, deep learning, Generative AI, and Agentic AI systems.

This role goes well beyond hands-on engineering. You will lead discussions directly with clients, identify and prioritize high-impact AI opportunities across both existing engagements and new requirements, design solution architectures, build rapid prototypes and Proofs of Concept (PoCs), and lead a team of AI/ML engineers to deliver production-grade systems at enterprise scale.

The ideal candidate combines deep, broad technical mastery across the AI/ML spectrum with business acumen, people leadership, and the ability to articulate AI strategy to both technical teams and executive audiences.

Key Responsibilities

Client Engagement & Use-Case Discovery

• Lead discussions with clients to understand strategic business challenges, operational bottlenecks, and transformation goals.
• Identify and prioritize potential AI use cases across existing engagements and new requirements, with clear articulation of business value and ROI.
• Facilitate AI discovery workshops and translate ambiguous business problems into well-defined AI solution opportunities.
• Create solution proposals, technical recommendations, implementation roadmaps, and effort estimates.
• Present AI capabilities, trade-offs, and business value to senior and executive stakeholders.
• Support pre-sales engagements through solution demonstrations, technical proposals, and client presentations.

AI/ML Solution Architecture & Delivery

• Own end-to-end delivery of AI initiatives from discovery and design through development, deployment, and production support.
• Architect solutions across the full AI/ML spectrum: predictive modeling, NLP, computer vision, recommendation systems, Generative AI, and agentic automation selecting the right approach for each problem.
• Design and develop enterprise-grade AI applications using Large Language Models (LLMs), Agentic AI frameworks, and modern AI engineering practices.
• Architect autonomous and multi-agent systems capable of reasoning, planning, orchestration, and tool execution.
• Build Retrieval-Augmented Generation (RAG) pipelines using enterprise knowledge sources.
• Integrate AI solutions with enterprise systems, APIs, databases, and cloud platforms.
• Optimize solutions for scalability, latency, cost, security, and reliability.

Proof of Concept (PoC) & Innovation

• Rapidly prototype AI solutions to validate technical feasibility and business impact.
• Define evaluation criteria and success metrics for AI pilots.
• Conduct benchmarking of AI/ML models, frameworks, and orchestration strategies.
• Evaluate emerging AI technologies and translate them into practical enterprise capabilities.
• Present findings, recommendations, and implementation approaches to clients.

Team Leadership & Capability Building

• Lead, mentor, and grow a team of AI/ML engineers; own delivery quality and technical direction.
• Conduct design and code reviews; establish engineering standards and best practices across the team.
• Drive hiring, onboarding, and capability development for the AI practice.
• Define reusable AI components, accelerators, and internal frameworks that speed up delivery.
• Collaborate with Product Managers, Business Analysts, Engineering teams, UX designers, and client stakeholders.

AI Architecture & Governance

• Design modular AI architectures following enterprise security, governance, and compliance standards.
• Establish guardrails for responsible AI, prompt engineering, evaluation, and model governance.
• Define model lifecycle management practices — versioning, monitoring, drift detection, and retraining.
• Contribute to AI engineering standards and architectural decision-making.

Required Technical Expertise

Programming

• Python (expert level)
• TypeScript / JavaScript
• SQL

Machine Learning & Data Science

• Supervised and unsupervised learning, ensemble methods, feature engineering
• Model evaluation, validation, and hyperparameter tuning
• Deep learning (PyTorch, TensorFlow/Keras)
• NLP, computer vision, time-series forecasting, and recommendation systems
• Statistical analysis and experimentation (A/B testing)
• Data processing at scale (Pandas, NumPy, scikit-learn, Spark a plus)

Generative & Agentic AI

• Large Language Models — selection, fine-tuning, and optimization
• Agentic AI and Multi-Agent Systems
• Retrieval-Augmented Generation (RAG)
• Prompt Engineering and context management
• AI Evaluation Frameworks and Model Observability
• Semantic Search and Embeddings

AI Frameworks & SDKs

• LangGraph, LangChain, LlamaIndex
• AutoGen, CrewAI, Semantic Kernel
• OpenAI SDK, Anthropic SDK, Google GenAI SDK

Cloud Platforms

Experience with one or more:

• AWS (Bedrock, SageMaker)
• Azure (AI Foundry / Azure OpenAI, Azure ML)
• Google Cloud (Vertex AI)

Backend & Integration

• FastAPI, REST APIs
• Event-driven architectures and microservices
• Enterprise system integrations

Data Platforms

• PostgreSQL, MongoDB
• Vector databases (Pinecone, Weaviate, Milvus, ChromaDB, FAISS)

DevOps & MLOps

• Docker, Kubernetes, Git, CI/CD
• ML pipelines and experiment tracking (MLflow or equivalent)
• Model deployment, monitoring, and drift management
• Infrastructure as Code (preferred)

Leadership & Consulting Skills

• Proven ability to lead client discussions and identify AI opportunities with measurable business outcomes.
• Experience translating ambiguous business requirements into technical solutions and delivery plans.
• Executive-level communication and presentation skills.
• Experience preparing solution proposals, architecture documents, and client presentations.
• Ability to balance technical feasibility, business value, implementation complexity, and ROI.
• Track record of mentoring engineers and building high-performing technical teams.

Preferred Experience

• 4–7 years of software engineering, ML engineering, or data science experience, including hands-on AI/ML delivery.
• Experience leading enterprise AI initiatives from discovery through production deployment.
• Experience working directly with enterprise clients or in consulting engagements.
• Hands-on experience designing autonomous AI agents and enterprise automation solutions.
• Familiarity with AI governance, responsible AI, and enterprise security principles.