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.