Description

We are seeking a strategic and technically strong leader to define and execute the organization’s AI/ML strategy and accelerate the adoption of artificial intelligence across products, engineering, and business functions. This role will lead the design, development, evaluation, and productionization of AI/ML solutions, with a strong focus on Generative AI, LLMs, AI agents, machine learning, intelligent automation, and AI-enabled software engineering. The ideal candidate combines deep technical expertise with strong business and organizational leadership skills and can translate emerging AI capabilities into scalable, secure, measurable business outcomes.

Key Responsibilities

• AI/ML Strategy & Roadmap
  • Define and execute the enterprise AI/ML strategy aligned with business and technology objectives.
  • Identify and prioritize high-value AI/ML use cases across products, engineering, and business operations.
  • Develop AI/ML roadmaps covering experimentation, adoption, productionization, and scale.
  • Evaluate emerging AI/ML technologies, models, platforms, and frameworks.
  • Establish standards and best practices for AI/ML development, deployment, evaluation, and governance.
• Generative AI & LLM Engineering
  • Lead development and implementation of Generative AI solutions using LLMs and multimodal models.
  • Design and implement RAG, agentic AI, tool use, function calling, and multi-agent architectures.
  • Develop LLM-powered applications and intelligent workflows.
  • Evaluate and select foundation models based on quality, latency, cost, security, and business requirements.
  • Establish LLM evaluation frameworks covering accuracy, relevance, hallucination, safety, latency, and cost.
  • Drive adoption of AI coding assistants and AI-powered software development practices.
  • Identify opportunities to use AI to improve developer productivity, testing, quality, and engineering efficiency.
• Machine Learning
  • Lead development and deployment of machine learning models across business and product use cases.
  • Define ML modeling, experimentation, training, validation, and deployment strategies.
  • Establish MLOps practices for model lifecycle management, monitoring, retraining, and governance.
  • Partner with Data Science and Data Engineering teams to build reliable ML pipelines and data platforms.
  • Apply statistical modeling, predictive analytics, classification, recommendation, anomaly detection, and optimization techniques where appropriate.
• AI Engineering & Architecture
  • Define scalable architectures for AI/ML applications and platforms.
  • Design AI systems integrating models, data, APIs, enterprise applications, and business workflows.
  • Establish patterns for model serving, inference, prompt management, vector databases, embeddings, and retrieval systems.
  • Design for scalability, reliability, observability, performance, and cost optimization.
  • Integrate AI/ML capabilities into existing enterprise technology ecosystems.
• AI Governance, Security & Responsible AI
  • Establish AI governance, security, privacy, and responsible AI practices.
  • Partner with cybersecurity, legal, compliance, data governance, and architecture teams.
  • Define controls for sensitive data, model access, prompt security, AI output validation, and model risks.
  • Establish AI risk assessment and approval processes for production deployments.
  • Ensure AI/ML solutions meet enterprise security, regulatory, and ethical requirements.
• AI Productization & Delivery
  • Lead AI/ML initiatives from ideation → experimentation → MVP → production → scale.
  • Establish processes for rapidly validating AI use cases while maintaining production engineering standards.
  • Define KPIs and success metrics for AI initiatives.
  • Measure business value, productivity improvements, quality improvements, cost savings, and adoption.
  • Work closely with Product, Engineering, Data, UX, Security, and business stakeholders to deliver AI-powered products and capabilities.
• AI Transformation & Leadership
  • Drive organizational adoption of AI/ML technologies.
  • Establish AI Centers of Excellence, communities of practice, or enablement programs.
  • Educate engineering and business teams on effective AI adoption.
  • Mentor AI/ML engineers, architects, data scientists, and technical leaders.
  • Build and scale high-performing AI/ML engineering teams.
  • Partner with executive leadership to communicate AI opportunities, risks, investments, and outcomes.

Required Qualifications

• 10 years of experience in software engineering, machine learning, data science, AI engineering, or technology leadership.
• 5 years of hands-on experience with AI/ML technologies.
• Demonstrated experience taking AI/ML solutions from experimentation to production.
• Strong experience with Generative AI and Large Language Models (LLMs).
• Experience designing RAG and agentic AI architectures.
• Strong understanding of machine learning fundamentals and model lifecycle management.
• Experience with cloud AI/ML platforms such as AWS, Azure, or GCP.
• Experience with Python and modern AI/ML frameworks.
• Strong understanding of APIs, distributed systems, data architectures, and cloud-native technologies.
• Experience with AI/ML evaluation, monitoring, observability, and governance.
• Excellent communication and executive stakeholder-management skills.

Preferred Technical Skills

• Large Language Models / Foundation Models
• Generative AI
• AI Agents / Agentic AI
• RAG / Retrieval Systems
• Prompt Engineering
• Embeddings & Vector Databases
• Model Fine-tuning
• LLM Evaluation
• Machine Learning & Deep Learning
• MLOps
• Python
• PyTorch / TensorFlow
• LangChain / LangGraph or equivalent frameworks
• Model APIs and AI platforms
• AWS / Azure / GCP
• Kubernetes / Docker
• APIs & Microservices
• Data Engineering & Data Pipelines
• AI Security & Responsible AI

Key Performance Indicators

• Number and quality of AI/ML use cases successfully moved into production
• Business value and ROI generated through AI initiatives
• AI adoption across engineering and business teams
• Improvement in software engineering productivity
• Reduction in development and testing cycle time
• Model accuracy, reliability, and performance
• LLM quality and evaluation scores
• AI application latency and infrastructure cost
• Reduction in manual processes through intelligent automation
• Adoption and effectiveness of AI developer tools
• Compliance with AI security, governance, and responsible AI standards

Key Skills

Education

Any Graduate