AI Practice Head

iProgrammer Solutions; Pune District; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

iProgrammer Solutions in Pune is seeking an experienced AI Lead to drive the 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, covering classical ML, deep learning, Generative AI, and agentic systems.

The role combines technical mastery with business acumen, people leadership, and the ability to communicate AI strategy to technical teams and executives.

Qualifications

• Extensive experience in AI/ML delivery for enterprise settings.
• Proven ability to lead client discussions and architect AI solutions.
• Hands-on with ML frameworks and AI governance principles.

Responsibilities

• Lead client conversations to identify AI opportunities and ROI.
• Own end-to-end AI delivery from discovery to production.
• Build PoCs and rapid prototypes for enterprise scale.
• Mentor engineers and drive capability development.

Skills

Python

TypeScript

SQL

ML Engineering

Leadership

Client Engagement

Tools

LangGraph

LangChain

LlamaIndex

OpenAI SDK

Job description

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.