AI Practice Head

Experience : 5-7 Years | Vacancies : 01 | Work Location : Pune | Work Mode : Work from Office (WFO) | Employment Type: Full Time Role

Role Summary:

We are seeking an accomplished AI Practice Head 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:

• 5–7 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.

About iProgrammer Solutions

iProgrammer Solutions, established with a mission to deliver innovative and efficient IT solutions, has been a trusted technology partner for businesses worldwide since its inception. Specializing in web app development, mobile applications, and digital transformations, we empower businesses to achieve their goals with cutting-edge technology and dedicated support.