AI Practice Head
- India
نُشرت أول أمس
عن الوظيفة
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.