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 recent 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.

📌 AI Practice Head (Pune)
🏢 Iprogrammer
📍 Pune